Mining Changes in Customer Purchasing Behavior - DiVA portal

2009:097

M A S T E R ' S T H E S I S

Mining Changes in CustomerPurchasing Behavior

- a Data Mining Approach

Samira Madani

Luleå University of Technology

Master Thesis, Continuation Courses Marketing and e-commerce

Department of Business Administration and Social SciencesDivision of Industrial marketing and e-commerce

2009:097 - ISSN: 1653-0187 - ISRN: LTU-PB-EX--09/097--SE

Abstract: The world around us is changing all the time. For businesses, knowing what is

changing and how it has changed is also crucial. One of the most important aspects of

surviving in a dynamic market is to know and adapt to changes happening in customer

behavior. In Fast Moving Consumer Goods (FMCG) Distribution Company, this issue

has more importance. Because of the variety of FMCGs products, distribution

companies and their different strategies, the purchasing behavior of customers may

change many times during a period and the competition become tougher. The purpose

of this study is to help Kalleh Company as a manufacturer and distributor of food

products in Iran market to mine changes happening in their customer behavior.

Mining changes has several steps includes data collection, data preprocessing, customer

segmentation, mining customer behavior patterns and change mining. For customer

segmentation, we use Customer Value Matrix. For mining pattern of behavior, we use

Apriori algorithm and maximal frequent itemsets. We have different kinds of changes

based on the literature, added/perished rules, emerging pattern and unexpected changes.

Also, there are two measures of similarity and unexpectedness to measure the change.

In this study, one time we calculate changes based on these measures from the

literature. Then, we modified these measures to calculate the difference between ordinal

attribute to bring their information in the calculation of changes. Our contribution is

modifying these change measure to bring more information and higher accuracy in

change mining. The result presented in the chapter4. Marketing managers can apply

these detected changes to be responsive accurately and timely to the changes in the

market. In addition, they can use it to evaluate different marketing campaigns to build

stronger relationship with their customer and knowing the market better. There are

many implications for mining changes in macro in micro aspects of businesses and also

in marketing campaigns and manufacturing.

2

Abstract: ................................................................................................................... 1

Chapter1: Introduction .............................................................................................. 8

1.1Background of the study: .................................................................................. 9

1.2Problem definition: ......................................................................................... 10

1.3Purpose of this study: ...................................................................................... 10

1.4Research question: .......................................................................................... 12

1.5Research motivation: ...................................................................................... 13

1.6Demarcation: .................................................................................................. 13

1.7Research outline: ............................................................................................ 13

Chapter2: Literature Review.................................................................................... 14

2. 1Mining Customer Behavior: ........................................................................... 15

2. 2Review of Data Mining .................................................................................. 15

2. 2.1 Data mining: in brief .............................................................................. 15

2. 2.2 Data mining Functions: ........................................................................... 17

2. 2.3 Classification in brief: ............................................................................ 18

2. 2.4 Clustering in brief: .................................................................................. 18

2. 2.5 Association Rules in Brief: ..................................................................... 19

2. 3Association Rule Mining Review: .................................................................. 19

2.3.1 Association Rule mining problem: ........................................................... 19

2.3.2 Apriori Algorithm.................................................................................... 20

2.3.3 Association Rule Mining Approaches: Apriori Approach ........................ 26

2. 4Mining Changes Literature Review: ............................................................... 30

2. 5Customer segmentation review: ..................................................................... 37

2. 5.1 Clustering Analysis ................................................................................ 38

2. 5.2 Customer Segmentation Model ............................................................... 38

2. 5.3 RFM Model ............................................................................................ 38

2. 5.4 RFM Scoring .......................................................................................... 39

2.5.5 Customer Value Matrix Model ................................................................ 43

3

Chapter3: Research Methodology ............................................................................ 45

3.1Research Methodology: .................................................................................. 46

3.2Research Design: ............................................................................................ 46

3.3Research Purpose: ........................................................................................... 46

3.4Research Approaches: ..................................................................................... 48

3.5Research Strategy: .......................................................................................... 48

3.6Research process:............................................................................................ 49

3.7Data Collection and Description: .................................................................... 50

3.8 Data Pre-Processing: ...................................................................................... 53

3.9Customer Segmentation: ................................................................................. 56

3.9.1 Customer Value Matrix ........................................................................... 57

3.9.2 An effective analytical tool ...................................................................... 57

3.9.3 Customer Value Matrix Methodology ...................................................... 58

3.10 Mining Customer Behavior: ......................................................................... 60

3.10.1 Association Rule Mining: ...................................................................... 60

3.10.2 Apriori algorithm: .................................................................................. 61

3.11 Change Mining: ........................................................................................... 63

3.11.1 Change Mining: ..................................................................................... 63

Chapter4: Results & Analysis .................................................................................. 70

4.1 Data preprocessing result: .............................................................................. 71

4.1.1 Data Cleaning .......................................................................................... 71

4.1.2 Data Transformation result: ..................................................................... 71

4.2Customer segmentation (in sql server 2000 ..................................................... 72

4.2.1 Customer Value Matrix Result: ............................................................... 72

4.3Customer Behavior Mining: ............................................................................ 75

4.3.1 Discretization Result: .............................................................................. 75

4.3.2 Association Rule Mining Results: ............................................................ 78

4.4Change Mining: .............................................................................................. 78

4

4.4.1 Some examples of change pattern: ........................................................... 79

4.4.2 Association rules and changes based (Chen et al, 2005): .......................... 80

4.4.3 Rules with discrete variables in RHS: ...................................................... 97

4.4.4 Change mining with Manhattan distance ............................................... 103

Chapter5: Conclusion, further research .................................................................. 123

5.1Conclusion: ................................................................................................... 124

5.2Our contribution: .......................................................................................... 126

5.3Limitation: .................................................................................................... 126

5.4Managerial Implication: ................................................................................ 126

5.5Future works: ................................................................................................ 127

References: ........................................................................................................... 127

List of tables

Table 2.1: Factors for classification of ARM…………………………………………..25

Table 2.2: Mining in a changing environment timetable………………………………37

Table3.1: Data collected from Kalleh Company………………………………………52

Table3.2: calculating variables for customer value matrix……………………………58

Table 4.1: RFM table fields…………………………………………………………….72

Table 4.2: calculating variables for customer value matrix…………………………...73

Table 4.3: calculating variables for customer value matrix…………………………...73

Table 4.4: segment information in for period 1………………………………………..74

Table 4.5: segment information in for period 2………………………………………..75

Table4.6: R quantile……………………………………………………………………76

Table4.7: M quantile…………………………………………………………………...76

Table4.8: F quantile……………………………………………………………………77

5

Table4.9: Area quantile………………………………………………………………..78

Table 4.10: Generated rule summary………………………………………………….78

Table 4.11: Generated Rules for period 1 Cluster 1…………………………………...80

Table4.12: Generated Rules for period 2 Cluster 1……………………………………81







Table4.19:Cat1 quantile….…………………………………………………………….98

Table4.20:Cat2 quantile…..……………………………………………………………99

Table4.21:Cat3 quantile …..………………………………………………….………100

Table4.22:Cat5 quantile ………………………………………………………………101

Table4.23:Cat11 quantile ….………………………………………………………….102

Table4.24:Cat13 quantile ……………………………………………………………..103

Table4.25: Generated Rules for period 1 Cluster 1, Change mining by (Chen et al, 2005)

measures & Manhattan distance………………………………………………………103


measures & Manhattan distance………………………………………………………104


measures & Manhattan distance……………………………….……………………...105


measures & Manhattan distance………………………………………………….….107


measures & Manhattan distance……………………………………………………..109

6


measures & Manhattan distance…………………………………………………...….110


measures & Manhattan distance……………………………………………………....112


measures & Manhattan distance……………………………………………………....117

List of figures:

Figure 2.1: Knowledge Discovery in Database Processes………………………...…...16

Figure 2.2 the major steps in data mining process…………………………………......17

Figure 2.3: Classification of DM techniques…………………………………………...17

Figure 2.4: Classic Problem of association rule mining ……………………………….20

Figure 2.5: Mining in a changing environment review………………………………...36

Figure 2.6: Customer Value Matrix…………………………………………………….44

Figure 3.1: Research design of this study………………………………………………46

Figure 3.2: Change mining process perspective………………………………………..49

Figure 3.3: Change mining process…………………………………………………….50

Figure 3.4: Change mining process in detail………………………………………..….50

Figure 3.5: Product categories of Kalleh company…………………………………….52

Figure 3.6: customer value matrix………………………………………………….….59

Figure 4.1: generalized product category……………………………………………....71

Figure 4.2: The Customer Value Matrix…………………………………………….....74

Figure4.3: R histogram……………………………………………………………..…..76

Figure4.4: M histogram………………………………………………………………..76

Figure4.5: F histogram…………………………………………………………………77

Figure4.6: Area histogram ……………………………………………………………..78

Figure4.7: Cat1 histogram…………………………………………………………..….98

7

Figure4.8: Cat2 histogram………………………………………………………….….99

Figure4.9: Cat3 histogram……………………………………………………………100

Figure4.10: Cat5 histogram…………………………………………………………...101

Figure4.11: Cat11 histogram …………………………………………………………102

Figure4.12: Cat13 histogram……………………………………………………….....103

8

Chapter1: Introduction

Background of the study

Problem definition

Purpose of this study

Research question

Research motivation

Research demarcation

Research outline

9

1.1Background of the study: The world around us changes continuously. Knowing and adapting to changes

is an important aspect of our lives. For businesses, knowing what is changing and

how it has changed is also essential (Liu et al, 2000). One of the most important

aspects of surviving in a dynamic market is to know and adapt to changes happening

in customer behavior. Moreover, in recent years, there has been the explosive

growth in the amount of information (Min, S., H., Han, I., 2005). In general, Fast

moving consumer goods (FMCG) distribution companies collected huge amount of

data from their customers and their purchasing transactions. In this gathered data,

we can find interesting hidden information about the customers and their behaviors.

The traditional approach for marketing decision making for marketing

promotions, campaigns and market research in FMCG distribution companies is to

focus more on their internal expert opinions. These experts include the marketing

managers and also sales managers who are in constant touch with their salespeople

and merchandisers who bring them market information.

However, this kind of decision making process ignores the customer data and

their behaviors. Furthermore, in today’s world where the market is highly

competitive and products are overwhelming, customers face with various products

and various providers with different marketing strategies (Hossein Javaheri, S.,

2008). In such a situation, customer behavior changes all the time due to such a

dynamic market (Chen et al, 2005). When the marketing manager became aware of

some changes in the market by sales team; he/she does not have any idea about how

and where to start understanding these changes and their reasons. It results to design

a wide time-consuming and costly market research which its result maybe did not

reach on time to the marketing department to react to these changes. Also in such a

market, there are many promotion campaigns by company itself and competitors

that it is difficult to analyze the effectiveness of them in the market. So, in the

competitive environment, there is a need to mine customer data and their

transactions to find changes in customer purchasing behavior which is an effective

and efficient way to respond to their needs timely and accurately. As a result, many

FMCG distribution companies in Iran are trying to move away from traditional way

10

for planning their marketing campaigns, promotions and market research by

understanding changes happening in their customers’ purchasing behavior. Change

mining helps managers to make better marketing strategies.

1.2Problem definition: Kalleh Company is a private manufacturer and distributor of food product in

Iran. It produces different categories of food product from dairy products to ice

cream and meats and sauces. It has more than 10 different categories and about 800

products. Now, the company is faced with the challenge of increasing competition.

There are some reasons behind it. First, according to the high variation of products,

it should compete in different food market like dairy, ice cream and meat. It results

to compete with many competitors with different product categories and different

marketing strategies. Also there are some powerful governmental companies that

make competition tougher for Kalleh. So in such a market, the customer behavior

may change by the trend of companies’ strategies in the market and also by

changing their need by themselves.

Kalleh Company in order to answer to the changes in customer purchasing

behavior timely and not being behind the customer needs and the competition need

to mine changes in the customer purchasing behavior. The goal of Kalleh Company

is to mine changes in purchasing behavior of the customers in different segments to

respond to these changes timely and accurately to increase its return on investment

(ROI).

1.3Purpose of this study: The purpose of this study is to mine changes in customer purchasing behavior.

In order to reach this goal we need to building customer purchasing patterns of

customers based on the customer, product and transaction data collected in

databases.

Data mining techniques can help us to reach this goal. According to (Song et

al, 2001), data mining is the process of exploration and analysis of large quantities

of data in order to discover meaningful pattern and rules. Many of data mining

Studies has focused on developing techniques to build precise models to predict

customer’s behavior, and to set up marketing strategies and customization.

According to (Nemati & Barko, 2001; cited by Nemati, H.R., Barko, C. D., 2003),

most of data mining applications (72%) are centered on predicting customer

11

behavior. Comparatively little attention has been paid to discover changes in

databases collected eventually (Liu et al., 2000). From literature review, what is

obvious is too much time spent on worrying about “absolute” numbers, like

Lifetime Value. However, what they should really be observing is “relative”

numbers – change over time. Highest potential ROI customers from a marketing

viewpoint are Customers who are in the process of changing their behavior either

accelerating their relationship with you, or ending their relationship with a company

(Novo, j., 2008). In many applications, mining changes can be more crucial than

producing precise prediction models, which are in the center of existing data mining

researches. Regardless of how the model is accurate, it is inactive by itself because it

can only predict based on patterns mined in the old data. Acting based on the built

model should not guide to actions that may change the environment because

otherwise the model will stop to be correct (Liu et al., 2000). Prediction model

building is more appropriate in areas where the environment is comparatively

steady. However, in many business conditions, constant human interference to the

environment is a fact. Businesses simply cannot let nature take its course. They

constantly need to do actions in order to provide better services and products by

finding the attractive changes and steady patterns in customer behaviors. Still in a

comparatively steady environment, changes are also unavoidable due to internal and

external issues (Liu et al., 2000).

From these viewpoints the question: ‘Which patterns exist?’ as it is responded

by state-of-the art data mining technology, is replaced by the question: ‘How do

patterns change?’ (Böttcher, M., et al, 2006). Actually, discovery of interesting and

earlier unidentified changes in customer, product and transaction data, not only let

the user monitor the influence of past business decisions but also to get ready

today’s business for tomorrow’s needs (Böttcher, M., et al, 2006).

Major changes often need instant concentration and actions to modify the

existing practices and/or to change the domain condition (Liu et al, 2000). By using

change mining methodology, Kalleh Company can detect different kinds of changes

happening in the customer purchasing behavior to build stronger relationship with

the customers. Also, understanding changes in customer behavior can assist

managers to set up effective and efficient promotion campaigns.

(Liu et al, 2000) mentioned that there are two main goals for mining changes

in a business environment:

12

"To follow the trends": The main feature of this kind of applications is the

word "follow". Companies like to know where the trend is going not to be left

behind. They need to investigate customers' changing behaviors so as to provide

products and services that suit the changing needs of the customers.

"To stop or to delay undesirable changes": In this kind of applications, the

keyword is "stop". Companies like to know undesirable changes as soon as possible

and to plan corrective measures to stop or to delay the pace of such changes.

The overall procedure consists of several steps. In the literature, there are some

methods for change mining in the dynamic situation. According to (Song et al,

2001), the majority of data mining techniques like association rules and neural

networks cannot be used alone because they cannot manage dynamic situation well.

(Song et al, 2001) and (Chen et al, 2005) developed a methodology for mining

changes. They used association rule to detect interesting association relationships

among a large set of data items which introduced by (Agrawal et al., 1993). The

methodology detects all kinds of changes. According to (Chen et al, 2005), Change

mining has several steps including data preprocessing, customer segmentation,

mining association rule and change mining. In the first customers are segmented

based on their behavioral variables, recency, frequency and monetary (RFM). Then

by building association rule with customer behavioral variable (RFM), customer

data and transaction data, we describe the customer purchasing behavior in two

different time snapshots, and in the end we compare generated rules for each

segment to mine changes in the customer purchasing behavior. To mine changes,

various algorithms and techniques should be used. In order to implement these

algorithms and techniques, an extensive programming is needed. Finally, we

combined all of the algorithms to build a change mining package.

1.4Research question: Based on the problem discussion that we have above, the purpose of this study

is to mine changes in customer purchasing behavior. In order to reach this purpose,

the research question will be as followed: How businesses can be responsive to the

changes of customer behavior in dynamic market. In addition, how businesses can

detect and access to the changes happened in the customer behavior pattern to be

responsive accurately and timely.

13

1.5Research motivation: Recently, we have watched an explosion of data produced and collected by

individuals and organizations. This fast growth in data and databases made the

problem of data overload (Li, X. B., 2005). More recently, increased computing

power has led to greater elasticity in the models one can use and the amount of data

that can be stored and processed (Bolton, R. J., 2004) and as a result, data mining

techniques have came out and flourished in the past several years to encounter this

demand (Li, X. B., 2005). Organizations are starting to understand the importance of

data mining in their marketing strategies.

In this situation, businesses currently face the challenge of a constantly

evolving market where customer needs are changing all the time (Chen et al, 2005).

In such an environment, knowing the changes and responding rapidly and correctly

to them, has a high importance. While customer needs change over time, if

businesses could not meet their need, they would lose their customers who are their

ROI resources. Some works have been done in change mining in retailing. One of

the businesses that change mining can help it to improve, is FMCG distribution

business that face a dynamic markets by huge variation of products and competitors

in the market. The purpose of the change mining is following the trends that are

happening in the customer purchasing pattern, detecting the changes and respond to

them timely to satisfy customers more and meet their needs.

1.6Demarcation: This study focus on mining changes in customer

purchasing behavior based on the customer and purchasing transaction stored in a

database. Change mining has been done by data gathered from a database of FMCG

Distributor Company in Iran. Most of the literature reviewed is about mining

changes in customer purchasing behavior. Our work focus on building customer

behavior patterns by association rule mining and the comparison of these built rules.

These patterns just based on their previous transactions.

1.7Research outline: This thesis consists of five chapters. The first chapter

is introduction that gives a brief background about subject followed by research

question, objectives, and motivation. Chapter 2 is a literature review, consists

literature review on data mining, association rule, change mining and customer

segmentation. Chapter3 is about our research methodology including data

preprocessing, market segmentation, mining customer behavior and change mining.

Chapter4 is about the results and analysis. Chapter 5 is the last chapter that contains

conclusion, limitation, and further research.

14

Chapter2: Literature Review

Review of Mining Customer Behavior

Review of Data Mining

Review of Association Rule Mining

Review of Change Mining

Review of Customer Segmentation

15

2. 1Mining Customer Behavior: Different methods to describe customer

behavior exist in the literature. Among them, there are various types of conjunctive

rules to build customer behavior pattern including association rules and

classification rules (Agrawal R. et al, 1996 & Breiman L., et al., 1984 cited on

Adomavicius, G., Tuzhilin, A., 2001)

Using rules to describe customer behavior has certain advantages. Besides

being descriptive way to portray behaviors, a conjunctive rule is a well-studied

concept and it is used widely in data mining, expert systems, and many other areas.

In addition, researchers have proposed many rule discovery algorithms in the

literature, especially for association rules (Adomavicius, G., Tuzhilin, A., 2001). To

discover rules that describe the behavior of customers, we can use various data

mining algorithms, like Apriori for association rule mining.

Association rules were initially applied for market basket analysis to find the

relationships between product items purchased by customers at retail stores

(Agrawal, Imielinski, & Swami, 1993; Srikant, Vu, & Agrawal, 1997 cited by Chen

et al, 2005). In a research of customer behavior, we can apply association rule to

find the correlations between customer demographic variables, purchased product

and product databases (Song et al, 2001).

In this chapter, we will have a review of data mining, then association rules.

Then the next topic will be the change mining of customer behavior in the literature.

And following by that finally we will have a brief review of customer segmentation.

2. 2Review of Data Mining

2. 2.1 Data mining: in brief

Today, size of databases can be very large. Within this data you can find

hidden strategic information. But when you have a huge amount of data, inducing

meaningful conclusions is not easy. The novel answer is data mining being used

both to increase revenues and to reduce costs. Many people use data mining as a

synonym for another popular word, Knowledge Discovery in Database. In rotation

other people define Data Mining as the core process of KDD.

The KDD processes are shown in Figure 2.1 (Han, J., & Kamber, M., 2006).

Usually KDD has three processes. First one is preprocessing executed before data

mining techniques applied to the right data. The preprocessing includes data

cleaning, integration, selection and transformation. The main process of KDD is the

data mining process. In this process different algorithm are applied to produce

16

hidden knowledge. The last process is post-processing comes evaluating the mining

result according to users’ requirements and domain knowledge.

Regarding the evaluation results, if the result is satisfactory the knowledge can

be presented; else we have to run some or all of those processes again till we get the

satisfactory result (Han, J., & Kamber, M., 2006).

Figure 2.1: Knowledge Discovery in Database Processes

(Song et al, 2001) defines data mining as a process of exploration and analysis

of large quantity of data to discover meaningful patterns and rules. (Feelders et al,

2000) define the process of data mining as follows:

17

Source: (Feedlers et al, 2000)

Figure 2.2 the major steps in data mining Process

The data mining returns potential is immense. Innovative organizations

worldwide are already using data mining to attract higher-value customers, to

configure their product offerings differently to increase sales, and to minimize losses

due to mistakes or fraud.

2. 2.2 Data mining Functions: (Dunham, 2002) categorizes data mining to two

categories, one is descriptive and the other one is predictive (Figure 2.3).

Source: (Dunham, 2002)

Figure 2.3: Classification of DM techniques

The first and simplest analytical step in data mining is to describe the data-

18

summarize its statistical attributes such as means and visual review like charts and

graphs, and correlations among variables. The most important step is right data

selection, data gathering and data exploration. Sometimes data description alone

cannot provide an action plan. You must build a predictive model based on patterns

determined from known results, and then examine that model with a new sample

data. A good model should never be the same as reality, but it can be a useful guide

to know your business. And after all we should empirically verify the model

(Twocrows.com, 2005). In the next section, we explain briefly three important data

mining techniques.

2. 2.3 Classification in brief:

Based on (Han and Kamber, 2006), Classification is automatic model building

that can classify a class of objects so as to predict the classification or missing

attribute value of future objects whose class may not be known. The process has 2

steps. In the first step, a model is built to describe the characteristics of a set of data

classes or concepts based on the collection of training data set. Because data classes

or concepts are predefined, this step is also known as supervised learning. In the

second step, the model is used to predict the classes of future data or objects. There

are several techniques for classification (Han and Kamber, 2006). In Classification

by decision tree many researches are done and plenty of algorithms have been

designed, Murthy did a extensive survey on decision tree induction (Murthy, 1998;

cited by Han, J., & Kamber, M., 2006). Bayesian classification is another technique

that can be found in (Duda and Hart, 1973 cited by Han, J., & Kamber, M., 2006).

Nearest neighbor methods are also talked about in many statistical texts on

classification, such as (Duda and Hart, 1973, cited by Han, J., & Kamber, M., 2006)

and (James, 1985, cited by Han, J., & Kamber, M., 2006). Besides, there are many

other machine learning and neural network techniques used to help building the

classification models.

2. 2.4 Clustering in brief: As we mentioned before, classification can be

taken as supervised learning process, clustering is another mining technique similar

to classification. However clustering is an unsupervised learning process.

"Clustering is the process of grouping a set of physical or abstract objects into

classes of similar objects" (Han, J., & Kamber, M., 2006), so that objects within the

same cluster must be similar to some extend, also they should be dissimilar to those

objects in other clusters. In classification each record belongs to a predefined class,

while in clustering there is no predefined class. In clustering, objects are grouped

together based on their similarities. (Han, J., & Kamber, M., 2006)Similarities

19

between objects are explained by some similarity functions; usually similarities are

quantitatively defined as distance or other measures by corresponding domain

experts. (Han, J., & Kamber, M., 2006) Most clustering applications are used in

market segmentation. When they cluster their customers into different groups,

business organizations can provide different personalized services to different group

of markets. (Han, J., & Kamber, M., 2006) An extensive survey of current clustering

techniques and algorithms is available in (Berkhin, 2002; cited by Han, J., &

Kamber, M., 2006).

2. 2.5 Association Rules in Brief:

Association rule mining is one of the most important techniques of data

mining. (Agrawal et al, 1993) introduced this method first time. The goal of this

technique is extracting interesting correlations, frequent patterns, and associations

among sets of items in the transaction databases or other data reservoirs (Agrawal et

al, 1993). Association rules are used extensively in various areas. In this study we

will use association rule to mine customer behavior pattern to find behavioral

changes. In the next section, we will have a review of association rule mining.

2. 3Association Rule Mining Review: 2.3.1 Association Rule mining problem: In this section, we will

introduce association rule mining problem in detail. A typical association Rule has

an implication of the form A B where A is an itemset and B is an itemset that

contains only a single atomic condition (Berry & Linoff, 2004). There are two

definitions to evaluate each association rule. The support of an association rule is the

percentage of records containing both A and B and the confidence of a rule is the

percentage of records containing itemset A that also contain itemset B. The support

shows the usefulness of a discovered rule and the confidence shows certainty of

found association Rules (Berry & Linoff, 2004). We can calculate another variable

called Lift. It Measures the difference between confidence and expected value of

confidence for a rule. (Berry & Linoff, 2004) define Lift (also called improvement),

as a measure telling us how much better a rule is at forecasting the result than just

assuming the result in the first place. “Lift is the ratio of the density of the target

after application of the left-hand side to the density of the target in the population”

(Berry & Linoff, 2004). “Another way of saying this is that lift is the ratio of the

records that support the entire rule to the number that would be expected, assuming

that there is no relationship between the products” (the exact formula is givenlater in

the chapter) (Berry & Linoff, 2004).

20

2.3.2 Apriori Algorithm Association rule mining is discovering association

rules that satisfy the pre-defined minimum support and confidence from a database

(Agrawal, R., & Srikant, R., 1994). According to (Agrawal, R., & Srikant, R.,

1994), this problem is usually decomposed into two sub problems:

One is to find those itemsets whose occurrences surpass a predefined threshold

in the database; those itemsets are called frequent or large itemsets. This problem

can be later divided into 2 sub problems: candidate large itemsets generation and

frequent itemsets generation process. Large or frequent itemsets are those itemsets

whose supports surpass the support threshold as and candidate itemsets are those

itemsets that are expected or have the hope to be large or frequent.

The second problem is producing association rules from those large itemsets

with the limits of minimal confidence. You can see the whole process of standard

problem of mining association rules in figure 2.3.

Source: (Agrawal et al, 1993)

Figure 2.4: Classic Problem of association rule mining

The whole performance of mining association rules is determined mainly by

the first step (Agrawal, R., & Srikant, R.). After the large itemsets are found, the

corresponding association rules can be derived in a straightforward manner. the

focus of most mining algorithms is counting of large itemsets Efficiently, and many

efficient solutions have been designed to target previous criteria (Kantardzic .M,

2003).

21

Different kinds of produced AR:

One attraction of association rules is the clarity and utility of the results, which

are in the form of rules about groups of products. There is a spontaneous attraction

to an association rule because it shows how tangible products and services group

together (Berry & Linoff, 2004). While association rules are easily understandable,

they are not always useful (Berry & Linoff, 2004). There are 3 types of generated

association rules: Actionable rules, trivial rules and inexplicable rules.

Actionable rules are the useful rule holds high-quality, actionable information.

Once the pattern is found, it is not often hard to justify, and thinking about rule in

the real environment can lead to insights and actions. Because the rule is easily

understood, it recommends plausible causes and possible interventions (Berry &

Linoff, 2004).

Another type of association rule is trivial rules. . Many people in business

know trivial results. Although it is valid and well supported in the data, it is still not

practical. A simple example is customers purchasing hamburgers buy hamburger

buns. A subtler problem drops within the same category. An apparently interesting

result may be the result of past marketing programs and product bundles. Although

other data mining techniques have this problem but market basket analysis is

vulnerable to reproducing the success of prior marketing campaigns because of its

dependence on un-summarized point-of-sale data, exactly the same data that defines

the success of the campaign. Trivial rules have one advantage and that is when a

rule should appear 100 percent of the time, the few cases where it does not hold

supply a lot of information about data quality. An area where business operations,

data collection, and processing may need to be more refined indicates the exceptions

to trivial rules (Berry & Linoff, 2004).

Inexplicable results seem to have no interpretation and do not recommend a

course of action. There is a caution and that is when applying market basket

analysis, many of the results are often either trivial or inexplicable; trivial rules

reproduce common knowledge about the business, which waste the effort used to

apply complex analysis techniques and Inexplicable rules are flukes in the data and

are not actionable (Berry & Linoff, 2004).

ARM Approaches Classification:

Association rule mining is a well studied research area; in this section, we will

only review some basic and classic approaches for association rule mining. As

22

mentioned before, the second sub-problem of ARM is straightforward; most of those

approaches focus on the first sub-problem. As mentioned, the first sub-problem can

be further divided into two sub-problems: candidate large itemsets generation

process and frequent itemsets generation process. Most of the algorithms of mining

association rules that surveyed are quite similar, the difference is the extent to which

specific improvements have been made. According to (Zhao, Q., Bhowmick, S.S.,

2003), there are 3 milestones in ARM classic problem; Apriori approach, tree

structure approaches and special issues in ARM. Besides these approaches, there is

another approach from (Zaki et al, 1999); class-based algorithms approach. There

some features that exists in literature to classify ARM algorithms by different

aspect. In the following subsection we will see some of them.

Here there are some features, which can be used to classify the algorithms. We

can categorize the algorithms based on several basic features that try to best

differentiate the various algorithms. These are different features that we have found

in literature (summarized in Table 2.1):

Target: Basic association rule algorithms actually find all rules with the

acceptable support and confidence thresholds. However, there are some more

efficient algorithms could be used. One approach which has been done to do this is

adding constraints on the rules which have been produced. Algorithms can be

categorized as complete (All association rules satisfying the support and confidence

are found), constrained (Some subset of all the rules are found, based on a technique

limiting them), and qualitative (A subset of the rules are produced based on

additional measures, beyond support and confidence, need to be satisfied) (Dunham

M.H., et al, 2001).

Type: Here we show the type of association rules which are produced (for

example regular (Boolean), spatial, temporal, generalized, qualitative, etc.)

(Dunham M.H., et al, 2001).

Data type: Besides data stored in a database, the type of data also is important.

Association rules of a plain text might be very important information to find out. For

example, “data”, “mining”, and “decision” may be highly dependent in a paper of

knowledge discovery (Dunham M.H., et al, 2001)..

Data source: In addition to market basket data, association rules of data absent

in the database might play important role for decision purposes of a company

(Dunham M.H., et al, 2001).

23

Technique: All approaches to date are based on first finding the large itemsets.

There could, of course, be other techniques not requiring that large itemsets first be

found. Although to date we are not aware of any techniques not generating large

itemsets, certainly this possibility does exist with the potential of improved

performance. However, (Agrawal et al, 1998) cited in (Dunham M.H., et al, 2001)

proposed “strongly collective itemsets” to evaluate and find itemsets. The term

“support” and “confidence” are completely different from large itemset approach.

An itemset I is said to be “strongly collective” at level K if the collective strength C

(K) of I as well as any subset of I is at least K (Dunham M.H., et al, 2001).

Itemset Strategy: Different algorithms consider the generation of items

differently. This feature shows how the algorithm considers transactions as well as

when the itemsets are produced. One technique, Complete, could produce and count

all potential itemsets. The most common approach is that introduced by Apriori.

With this strategy, a set of itemsets to count is produced prior to scanning the

transactions. This set remains constant during the process. A dynamic strategy

produces the itemsets during the scanning of the database itself. A hybrid technique

generates some itemsets prior to the database scan, but also adds new itemsets to

this counting set during the scan (Dunham M.H., et al, 2001).

Transaction Strategy: Different algorithms consider the set of transactions in a

different manner. This feature shows how the algorithm scans the set of transaction.

The complete strategy checks all transactions in the database. With the sample

approach, some subset of the database (sample) is checked prior to processing the

complete database. The partition techniques divide the database into partitions. The

scanning of the database requires that the partitions be checking individually and in

order (Dunham M.H., et al, 2001).

Itemset Data Structure: As itemsets are produced, different data structures can

be applied to keep track of them. The most usual approach seems to be a hash tree.

Alternatively, a trie or lattice may be applied. At least one technique suggests a

virtual trie structure where only a portion of the complete trie is actually

materialized (Dunham M.H., et al, 2001).

Transaction Data Structure: "Each algorithm assumes that the transactions are

stored in some basic structure, usually a flat file or a TID list" (Dunham M.H., et al,

2001).

Optimization: Many algorithms have been introduced improving on earlier

24

algorithms by applying an optimization strategy. Various strategies have considered

optimization based on available main memory, whether or not the data is skewed,

and pruning of the itemsets to be counted (Dunham M.H., et al, 2001).

Architecture: As indicated, the goal of some algorithms is working like

sequential function in centralized single processor architecture. Alternatively,

algorithms have been designed to work in a parallel manner suitable for a

multiprocessor or distributed architecture (Dunham M.H., et al, 2001).

Parallelism Strategy: Parallel algorithms can be more described as task or data

parallelism (Dunham M.H., et al, 2001).

In the literature there some other features that based on them also we can

categorize the association rule mining methods; in the following we can consider

them:

Counting Strategy: This refers to the methods used in counting the candidate

itemsets occurrences. There horizontal counting and vertical intersection are two

main approaches. The horizontal counting decides about the support value of a

candidate itemset by scanning transaction singly, and increasing the counter of the

itemset if it is a subset of the transaction. This approach operates well for a rarely

occurred candidate because only those transactions containing that itemset need to

be checked. The candidate look up operation, however, is very expensive for

candidates of large size (Su, J. H., Lin, W. Y., 2004). On the other hand, vertical

intersection is applied when the database is in a vertical format such that every

record is associated with an item to store the identifiers of the transactions

containing that item, called Tidlist. Despite the vertical intersection scheme omits

the I/O cost for database scan, it has the following shortage: when a candidate

itemset has a support count completely less than the number of transactions, a large

amount of unnecessary intersections happens there (Su, J. H., Lin, W. Y., 2004).

Search direction: according to (Su, J. H., Lin, W. Y., 2004), there are two main

methods for search direction, Bottom-up traversal and Top-down traversal. Today,

most Apriori-like approaches apply bottom-up traversal of the search space, which

starts from all frequent 1-itemsets upward to the longest frequent itemsets. The most

important advantage of this model is that it can effectively prune the search space by

exploiting downward closure property: when it recognized one itemset as

infrequent, all of its superset is also infrequent. However, this benefit fades when

most of the maximal frequent itemsets locating near the largest itemset of the search

25

lattice, due to a comparatively small support threshold. In this situation, there are

very few itemsets to be pruned (Su, J. H., Lin, W. Y., 2004). Another itemset

traversal method is Top-down traversal which applied in the opposite direction, i.e.

starting from the longest itemsets downward to the frequent 1-itemsets, or top-down

for short (Su, J. H., Lin, W. Y., 2004). This strategy is traditionally applied for

discovering maximal frequent itemsets (Tseng, M.C. & Lin, W.Y., 2001; cited by

Su, J. H., Lin, W. Y., 2004) But we should consider that though all of the frequent

itemsets can be derived from their maximal ones, more counting strategies are

needed to gain their exact supports for computing the confidences of association

rules. At the same time, if there are huge numbers of items and/or the support

threshold is very low; many infrequent itemsets have to be visited before the

maximal frequent itemsets are identified. This is why most work on frequent

itemsets mining accepts and applies the bottom-up paradigm instead. (Su, J. H., Lin,

W. Y., 2004).

Search strategy: While the search direction directs the way that the search

space is exploited, the search strategy identifies the order in which itemsets are

visited (Su, J. H., Lin, W. Y., 2004). One of these strategies is BFS. Most Apriori-

like algorithms apply breadth-first search (BFS) because it can facilitate the pruning

of candidates with downward closure. This strategy, however, needs more memory

to keep the frequent subsets of the pruned candidates (Su, J. H., Lin, W. Y., 2004).

Another strategy is DFS; recursively visiting the descendants of an itemset. In the

literature, this strategy is usually combined with the counting strategy of vertical

intersection because it is enough to keep in memory the tidlists corresponding to the

itemsets on the path from the root down to the presently inspected one. (Su, J. H.,

Lin, W. Y., 2004)

Table 2.1: Factors for classification of ARM

DIMENSION VALUES

Target Complete, Constrained, Qualitative

Type Regular (Boolean), Generalized, Quantitative, etc.

Data type Database Data, Text

Data source Market Basket, Beyond Basket

26

Technique Large Itemset, Strongly Collective Itemset

Itemset Strategy Complete, Apriori, Dynamic, Hybrid

Transaction Strategy Complete, Sample, Partitioned

Itemset Data Structure Hash Tree, Trie, Virtual Trie, Lattice

Transaction Data

Structure

Flat File, TID

Optimization Memory, Skewed, Pruning

Architecture Sequential, Parallel

Parallel Strategy None, Data, Task

Pattern Kind Sequential Pattern, Frequent Itemset, Structured

Pattern

Rule Kind Association Rule, Strong gradient relationship,

correlation(Han book)

Counting Strategy Horizontal, Vertical

Search Strategy Bottom-Up Traversal, Top-Down Traversal ,

Hybrid

Search Direction BFS, DFS

Candidate generation Complete, Heuristic

2.3.3 Association Rule Mining Approaches: Apriori Approach

AIS Algorithm:

The AIS (Agrawal, Imielinski, Swami) algorithm was the first algorithm

suggested for mining association rule in (Agrawal et al, 1993). It concentrates on

improving the quality of databases simultaneously with necessary functionality to

process decision support queries. According to (Zhao, Q., Bhowmick, S.S., 2003), in

this algorithm only one item consequent association rules are produced. It means

that the consequent of those rules only contain one item, for example we only

27

produce rules like ABC→D but not rules like AB→CD.

Disadvantage: The main disadvantage of the AIS algorithm is too many candidate

itemsets that at last turned out to be small are produced, needing more space and

wastes much effort that turned out to be useless. At the same time this algorithm

needs too many passes over the whole database (Zhao, Q., Bhowmick, S.S., 2003).

3.3.3.3 Apriori Algorithm:

Apriori is a great improvement in the history of association rule mining,

Apriori algorithm was first introduced by Agrawal in (Agrawal, R., & Srikant, R.,

1994). The AIS is just a straightforward approach that needs many passes over the

database, which produces many candidate itemsets and saving counters of each

candidate while most of them turn out to be not frequent. Apriori is more efficient

during the candidate generation process for two reasons; Apriori applies a different

candidate's generation method and a new pruning technique (Zhao, Q., Bhowmick,

S.S., 2003).

a) Problem & limitation of Apriori:

One is the complex candidate generation process that spends most of the time,

space, and memory. Another bottleneck is the several scan of the database. Many

new algorithms were designed with some modifications or improvements based on

Apriori algorithm. Commonly, there were two approaches:

First approach tries to reduce the number of passes over the whole database or

replace the whole database with only part of it based on the current frequent

itemsets. The other approach tries exploring different types of pruning techniques to

make the number of candidate itemsets much lesser. Apriori-TID and Apriori-

Hybrid (Agrawal, R., & Srikant, R., 1994), DHP (Park et al, 1995; cited by Zhao,

Q., Bhowmick, S.S., 2003), SON (Savesere et al, 1995) are modifications of the

Apriori algorithm (Zhao, Q., Bhowmick, S.S., 2003).

3.3.3.4 Optimized Apriori algorithms:

According to problems of Apriori, which have been mentioned in previous

section, some new approaches are introduced. In the following section we will have

them; item pruning and database passes over reduction.

a) Transaction and Item Pruning:

This is one of the main optimization of the Apriori Algorithm. There is no

28

need to inspect the whole database each time it is needed to count occurrence of

candidate itemsets. This optimization reduced drastically the needed time to count

the support for the candidate sets and enhances the performance. Transaction

pruning was present in 2 algorithms; AprioriTid, Apriori Hybrid and DHP.

AprioriTID, Apriori Hybrid:

AprioriTID was introduced in the same paper with Apriori. For all that, it does

not state it explicitly, it uses transaction pruning to improve Apriori performance.

The main difference comes from where it does not use the whole database to count

support for candidate sets, and it uses another approach (Ayad, A. M., 2000).The

main disadvantage of this algorithm is the size of the alternative set that shows the

database may go beyond the size of the actual database in early stages thus loosing

its edge on Apriori. Because of this disadvantage another algorithm, Apriori Hybrid

introduced. It uses Apriori at the first stages and then shifts to AprioriTID when

transaction pruning is more effective (Ayad, A. M., 2000).

DHP:

DHP (Dynamic Hashing and Pruning algorithm) is another algorithm that

introduced by (Park et al, 1995). It uses probabilistic counting to decline the number

of candidate itemsets counted during each round of Apriori execution. This decline

is completed by subjecting each candidate k ¬itemset to a hash-based filtering step

in addition to the pruning step (Ayad, A. M., 2000).

Throughout candidate counting in round k -1, the algorithm builds a hash

table. Each entry in the hash table is a counter that retains the sum of the supports of

the k-itemsets that correspond to that exacting entry of the hash table. The algorithm

uses this information in round k to prune the set of candidate k-itemsets. After

subset pruning as in Apriori, the algorithm can remove a candidate itemset if the

count in its hash table entry is smaller than the minimum support threshold.

According to, It has 2 advantage, first this algorithm is also based on the monotone

Apriori property, where a hash table is built for the purpose of reducing the

candidate space by pre-computing the proximate support for the k+1 item set while

counting the k-itemset. DHP has another important advantage, the transaction

trimming, which has been applied by removing the transactions that do not contain

any frequent items. However, this trimming and the pruning properties caused some

problems that made it impractical in many cases (Ayad, A. M., 2000).

29

b) Reducing the number of database passes:

As mentioned before, the main disadvantage of the classical Apriori is the

several passes it has to do on the databases the number of which is equal to the

length of the longest frequent itemset (Pattern) present in the database(Zhao, Q.,

Bhowmick, S.S., 2003). Many optimization efforts focused on eliminating the

number of database passes. They differed, however, in how the number of passes

decreased. This is the focus of this section.

Database Partitioning:

(Savasere et al, 1995) developed Partition, an algorithm that requires only two

scans of the transaction database. The database is divided into disjoint partitions,

each small enough to fit in memory. In a first scan, the algorithm reads each

partition and computes locally frequent itemsets on each partition using Apriori. In

the second scan, the algorithm counts the support of all locally frequent itemsets

toward the complete database. If an itemset is frequent with respect to the complete

database, it must be frequent in at least one partition; therefore, the second scan

counts a superset of all potentially frequent items.

The main achievement of Partition is the reduction of database activity. It was

shown that this reduction was not obtained at the expense of more CPU utilization.

It was shown however, that the number of partition greatly affects the performance

of the algorithm by affecting the number of locally frequent itemsets that turn to be

globally infrequent. The algorithm was shown to be vulnerable to data skew (Ayad,

A. M., 2000).

Dynamic itemset counting:

(Brin et al, 1997) proposed the Dynamic Itemset Counting algorithm. DIC

partitions the database into several blocks marked by start points and repeatedly

scans the database. In contrast to Apriori, DIC can add new candidate itemsets at

any start point, instead of just at the beginning of a new database scan. At each start

point, DIC estimates the support of all itemsets that are currently counted and adds

new itemsets to the set of candidate itemsets if all its subsets are estimated to be

frequent (Brin et al, 1997). If DIC adds all frequent itemsets and their negative

border to the set of candidate itemsets during the first scan, it will have counted each

itemset’s exact support at some point during the second scan; thus DIC will

complete in two scans (Ayad, A. M., 2000). The Dynamic Item set Counting (DIC)

30

reduces the number of I/O passes by counting the candidates of multiple lengths in

the same pass. DIC performs well in cases of homogenous data, while in other cases

DIC might scan the databases more often than the Apriori algorithm.

c) Both Sampling:

(Toivonen, H., 1996) proposed a sampling based algorithm that typically

requires two scans of the database. The algorithm first takes a sample from the

database and generates a set of candidate itemsets that are highly likely to be

frequent in the complete database. In a subsequent scan over the database, the

algorithm counts these itemsets’ exact supports and the support of their negative

border. If no itemset in the negative border is frequent, then the algorithm has

discovered all frequent itemsets. Otherwise, some superset of an itemset in the

negative border could be frequent, but its support has not yet been counted. The

sampling algorithm generates and counts all such potentially frequent itemsets in a

subsequent database scan (Toivonen, H., 1996). The algorithm was shown to

perform well compared to other level-wise algorithms and to be the Partition

algorithm. The database activity is reduced effectively one pass. The only drawback

of the algorithm, however, is that it has to test many spurious candidates due to the

reduced support threshold and to guarantee a superset of the actual frequent itemsets

(Ayad, A. M., 2000).

Conclusion of Apriori Approach:

Most of the algorithms introduced above are based on the Apriori algorithm

and try to improve the efficiency by making some modifications, such as reducing

the number of passes over the database; reducing the size of the database to be

scanned in every pass; pruning the candidates by different techniques and using

sampling technique (Zhao, Q. & Bhowmick, S.S., 2003). However, there are two

bottlenecks of the Apriori algorithm: first bottleneck is the complex candidate

generation process that uses most of the time, space, and memory and the other

bottleneck is the multiple scan of the database, Apriori is used in many applications

for building patterns in large databases

2. 4Mining Changes Literature Review: As it is mentioned in chapter

of customer behavior analysis, mining changes has very important role in business

strategies and marketing. In this chapter, we are going to briefly review the literature

related to mining changes in customer buying behavior patterns and determine the

position of my work within these researches. In the past, researchers generally

31

applied statistical surveys to study customer behavior. Recently, however, data

mining techniques have been adopted to describe and predict customer behavior

(Giudici & Passerone, 2002; Song, Kim, & Kim, 2001 cited by Song et al, 2001).

There are some related works to mining in a dynamic environment; of course, they

are not as much as work has been done in customer behavior modeling or

prediction.

Liu et al. (2000) devised a method of change mining in the context of decision

trees for predicting changes in customer behavior. Since decision tree is a

classification-based approach, it cannot detect complete sets of changes (Song et al,

2001). Association rule extraction was widely used for analyzing the correlation

between product items purchased by customers, and to support sales promotion and

market segmentation (Changchien & Lu, 2001; Changchien, Lee, & Hsu, 2004;

cited by Song et al, 2001). (Song et al, 2001) employed an approach based on

association rules to identify changes in customer behavior. (Chen et al, 2005)

employed another approach to recognize changes in customer behavior by

association rule mining methods.

There are existing works that have been carried out on learning in a changing

environment (Fruend and Mansour, 1997; cited by Song et al, 2001, Helmbold &

Long, 1994; cited by Song et al, 2001; Widmer, 1996; cited by Song et al, 2001).

There are some existing works in mining in a changing environment (Bay and

Pazzami, 1999; Ganti, Gehrk, Ramakrishnan, 1999; Han, Kamber, 2001; Liu et al,

2000; Nakhaeizadeh, Taylor, Lanquillon, 1998; cited by Song et al, 2001). For

example (Fruend and Mansour, 1997 cited in Song et al, 2001) presents a model of

learning in a changing distribution. All the following related works focus on

dynamic aspects or comparison between two different datasets or rules. They are

clustered as six categories in this chapter.

According to (Song et al, 2001) there are six groups of works in the area of

data mining in changing environment. These are as follows:

The first field of study that studies mining in a changing environment is rule

maintenance (Cheung, Han, Ng & Wong, 1996a; Cheung, Ng, Tam, 1996b;

Feldman, Aumann, Amir & manila, 1997; cited by Song et al, 2001, Thomas,

Bodagal, Alsbati & Ranka, 1997) the purpose of these studies is improving accuracy

in a changing environment. For example is the study that has been done by (Thomas

et al, 1997) which proposed an incremental updating technique based on Negative

32

Borders, for the maintenance of the association rules when new transaction data is

added to or eliminated from transaction database. An important aspect of this

technique is it requires a full scan of database if the database update causes the

negative border of the set of large itemsets to expand. But these techniques don’t

provide any changes to the user, they just maintain existing knowledge.

The second research trend associated to our work is discovering emerging

pattern (Agrawal, R. & Psaila, G., 1995; Dong, G., & Li, J., 1999; Li et al, 2000).

These researches try to find emerging patterns (EPs) which are described as itemsets

whose supports boosted significantly from one dataset to another. (Agrawal, R. &

Psaila, G.) established Active data mining paradigm which is in this paradigm, data

is continuously mined at a desired frequency. As rules are discovered, they are

added to a rulebase, and if they already exist, the history of the statistical parameter

associated with the rules is updated. When the history begins exhibiting certain

trends, specified as shape queries in the user-specified triggers, the triggers are fired

and appropriate actions are initiated. (Dong, G., & Li, J., 1999) introduced the data-

mining problem of emerging patterns (EPs). (Li et al, 2000) proposed the use of

jumping emerging patterns (JEPs) as the basis for a new classifier called the JEP-

Classifier. Each JEP can capture some crucial difference between a pair of datasets.

Then, aggregating all JEPs of large supports can produce more potent classification

power. They use two algorithms for constructing the JEP-Classifier which are both

scalable and efficient. These algorithms make use of the border representation to

efficiently store and manipulate JEPs. EPs can capture emerging trends in time-

stamped database, or useful contrast between data classes, but they don’t consider

the structural changes in the rules (Song et al, 2001).

Another connected research is subjective interestingness in data mining (Liu &

Hsu, 1996; Liu et al, 1997; Liu, et al, 1999; Padmanabhan & Tuzhilin, 1999,

Silberchatz & Tuzhilin, 1996; Suzuki, 1997 cited by Song et al, 2001). These

researches provide a number of techniques for finding unexpected rules regarding

users existing knowledge. For example, (Liu & Hsu, 1996) tries to link the gap

between the user and the rules created by an induction system. A fuzzy matching

technique is recommended for rule comparison in the context of classification rules.

It permits the user to compare the produced rules with his/her hypotheses or existing

knowledge in order to find out what is right and what is wrong about his/her

knowledge, and to tell what has changed since the last learning. This technique is

also helpful in data mining for solving the interestingness problem. (Liu et al, 1997

cited in Song et al, 2001) studies the problem of analyzing discovered rules next to a

33

particular form of existing concepts, namely general impressions (GIs). A

specification scheme for representing GIs is proposed and two matching algorithms

for analyzing discovered rules are presented. This technique is useful for solving the

interestingness problem. (Padmanabhan & Tuzhilin, 1999 cited in Song et al, 2001)

proposed a new definition of unexpectedness of a rule with respect to a belief and

showed an algorithm that finds unexpected association rules from data using this

measure. (Silberchatz & Tuzhilin, 1996 cited in Song et al, 2001) mentioned that

Measures of interestingness of patterns in data mining applications can be

categorized into objective and subjective and they classified subjective measures

into unexpected and actionable and argued, at the intuitive level, that these two

measures of interestingness are independent of each other. Although action ability

emerges to be the major concept, we believe that it is a difficult notion to capture

formally since they consider that most unexpected patterns are actionable and most

actionable patterns are unexpected, in this paper, they proposed to capture action

ability via unexpectedness. Consequently, they studied "unexpectedness" as a

measure of interestingness and described interestingness of a pattern in terms of how

strongly it "shakes" the existing system of beliefs. By this meaning they also make

unexpected patterns more interesting than the expected ones. All of the above work

study subjective measures of interestingness, but these techniques can not be applied

for detecting changes, as its analysis only compares each newly generated rule with

each existing rule to discover degrees of difference, and it doesn’t find which aspect

have changes, what kind of changes have taken place and how much change has

happened.

The forth research stream is mining from time-series data. There is an

interesting interest to find out regularity from time-series data (Das et al, 1997; Das

et al, 1998; Han, Dong & Yin, 1999 cited in Song et al, 2001). (Dos et al. 1998 cited

in Song et al, 2001) believe the problem of finding rules relating patterns in a time-

series to other patterns in that series, or patterns in one series to pattern in another

series, in fact they stress is in the discovery of local patterns in multivariate time

series in contrast to the traditional time series analysis which mainly focuses on

global models, and (Han et al., 1999 cited in Song et al, 2001) present several

algorithms for efficient mining of partial periodic patterns, by exploring some

interesting properties connected to partial periodicity. (Dos et al, 1997 cited in Song

et al, 2001) also presents an intuitive model for measuring similarities between two

time series, this model takes into account outliers, different scaling functions and

variable sampling rates; but these studies are rather different from my research

34

which centers on the detection of irregularity rather than regularity form data.

The fifth research field is mining class comparison to differentiate between

different classes (Bay, D.S. & Pazzani, M., J., 1999; Ganti et al, 1999 cited in Song

et al, 2001; Han, J., & Kamber, M., 2006). (Ganti et al, 1999 cited in Song et al,

2001) presents the general framework for measuring changes in two models. They

develop FOCUS Framework for calculating an interpretable, suitable deviation

measure between two datasets to compute the differences between “interesting”

characteristics in each dataset. Fundamentally, the difference between the two

models is quantified as the amount of work needed to change one model into the

other. Their framework work covers a wide variety of models as well as frequent

itemsets, decision tree classifier, and clusters, and captures standard measures of

deviation such as misclassification rate and the chi-square metric as special cases. It

offers deviation measures between the two mining model and focused regions but

cannot be directly used to detect customer behavior changes because it doesn’t

provide which aspects are changed and which kind of changes have occurred. (Bay,

D.S. & Pazzani, M., J., 1999); (Han, J., & Kamber, M., 2006) also provide

techniques for understandings the differences between several contrasting groups,

but these techniques can only identify change about the same structured rule.

Finally, (Liu et al, 2000) presents a technique for change mining by

overlapping two decision trees which are produced from different time snapshots,

but this change mining technique using decision trees cannot identify complete sets

of change. Since decision trees techniques run within a specified objective class,

only changes about designated consequent attributes can be detected. This approach

can be applied only in cases which have a precise research question. Also, this

technique doesn’t offer any information for the degree of change. (Song et al, 2001)

had a research on Understanding and adapting to changes of customer behavior for

an internet-based company. The aim of that research is to develop a methodology

which discovers changes of customer behavior automatically from customer profiles

and sales data at different time snapshots. They defined the 3 types of changes:

emerging pattern, unexpected changes and added/perished rules, then, similarity and

difference measures for rule matching to detect all types of changes. Finally, the

degree of change is assessed to detect significantly changed rules and rank them.

Their proposed methodology can evaluate the degree of changes as well as finds all

kinds of changes automatically from different time snapshot data. Another related

research has been done by (Chen et al, 2005) which integrates customer behavioral

variables, demographic variables, and transaction database to found a method of

35

mining changes in customer behavior. The behavioral variables, RFM, coupled with

growth matrix of customer value, are used to estimate the value that individual

customers give to the business. Association rules are used to identify the association

between customer profile and product items purchased. For mining change patterns,

two extended measures of similarity and unexpectedness are designed to study the

degree of similarity between patterns at different time periods. Finally, an online

query system provides marketing managers a tool for fast information search, and

valuable information based on timely feedback. (Cho et al, 2005) have done a

research for finding changes in customer buying behavior for recommendation

systems and it declared that the needs of customer changes over time so we should

take into consideration changes in customer preferences to progress the accuracy of

the recommendations made. They suggest a new methodology for improving the

quality of Collaborative Filtering (CF) recommendation that uses customer purchase

sequences. The proposed methodology is used to a large department store in Korea

and compared to existing CF techniques. Different experiments using real-world

data show that the proposed methodology provides higher quality recommendations

than do classic CF techniques, with better performance, particularly with regard to

heavy users. (Au & Chan, 2005 cited in Song et al) present another technique to

find changes in association rules. They present the meaning of the problem of

mining changes in association rules over time. The proposed approach permits

different fuzzy data-mining techniques to be used for tackling this problem. Given a

set of database partitions, each of which encloses a set of transactions gathered in a

specific time period, a set of association rules is found in each database partition.

They suggest executing data mining in the discovered association rules so as to

expose the regularities governing how the rules change in different time periods.

They proposed to use linguistic variables and linguistic terms to represent the

changes in the discovered association rules. Particularly, fuzzy decision trees are

built to discover the changes in the discovered association rules. The fuzzy decision

trees are then exchanged to a set of fuzzy rules, called fuzzy meta-rules because they

are rules about rules. By doing so, the changes hidden in the data can be exposed

and presented to human users in a comprehensible form. In addition, the discovered

changes can also be used to forecast any change in the future.

36

Figure 2.5: Mining in a changing environment review

Learning in a Changing

Environment

-FRUEND and

Mansour, 1997

-Helmbold & Long,

1994 cited by (Song et

al, 2001)

Rule

Maintenance

Emerging

Patterns

Subjective

interestingne

Mining from Time

Series Data

Mining Class

Comparisons

Mining

changes

-Cheung, Han, Ng

& Wong, 1996a;

-Cheung, Ng, Tam,

1996b;

-Cheung et al,

1997

-Agrawal &

Psaila, 1995

-Dong & Li, 1999

-Li, Dong &

Ramamohanarao,

2000

-Liu & Hsu,

1996; -Liu et al,

1997

-Liu, Hsu, Ma *

Chen, 1999

-Padmanabhan &

Tuzhilin, 1999

cited by (Song et

al, 2001)

-Silberchatz &

Tuzhilin, 1996

-Suzuki, 1997

-Das,

Gunopulous &

Mannila, 1997

-Das, Lin,

Manila,

Renganathan &

Smyth, 1998

-Han, Dong &

Yin, 1999

-Bay & Pazzani,

1999;

-Ganti et al,

1999; -Han, J., &

Kamber, M.,

2001

-Song et al, 2001

-Liu et al, 2000

-Chen et al, 2005

Mining in a Changing

Environment

37

Table 2.2: Mining in a changing environment timetable

Mining

Changes

• •

•

class

comparison

•

•

Time Series • • •

Subjective

Interestingness

• • • •

Rule

Maintenance

• •

Emerging

Pattern •

• •

Subject/ Year 1995 1996 1997 1998 1999 2000 2001

2002

2003 2004 2005

2. 5Customer segmentation review: The mass marketing approach

cannot satisfy the needs of varied customers today. This variety should be satisfied

using segmentation that splits markets into customer clusters with similar needs

and/or features that are likely to show similar purchasing behaviors (Dibb & Simkin,

1996 cited by Tsai, C., Y., Chiu, C., C., 2004). Segmentation theory suggests that

groups of customers with similar needs and purchasing behaviors are likely to show

a more homogeneous answer to marketing programs that aim specific consumer

groups (Tsai, C., Y., Chiu, C., C., 2004). Market segmentation has accordingly been

regarded as one of the most vital elements in achieving successful modern

marketing and customer relationship management (CRM) (Berson, Smith, &

Thearling, 2000 cited by Tsai, C.,Y., Chiu, C.,C., 2004).

Segmentation variable selection is a critical concern for successful market

segmentation."Segmentation variables can be classified into general variables and

product specific variables" (Wedel & Kamakura, 1997 cited by Tsai, C., Y., Chiu,

C., C., 2004). The general variables consist of the customer demographics and

38

lifestyles. The product specific variables entail customer purchasing behaviors and

intentions. Many researches have been done to use general variables to segment

customers because the variables are intuitive and easy to operate (Beane & Ennis,

1987; Hammond et al., 1996 cited by Tsai, C., Y., Chiu, C., C., 2004). Market

segmentation based on general variables is more instinctive and easier to conduct

than product specific variables. But the assumption that customers with alike

demographics and lifestyles will show similar purchasing behavior is unsure (Tsai,

C., Y., Chiu, C., C., 2004). Here, we have briefly, reviewed some of segmentation

methods from literature.

2. 5.1 Clustering Analysis Data mining is a type of analytic method for

summarizing useful knowledge and realizing useful data mode from huge data (Wu,

J., Lin, Z., 2005). In market research field, clustering is an effective and commonly

used method for market segmentation, realizing targeted market and segments of

customers. Clustering can be used as an independent tool to show data distribution,

monitor cluster’s characteristics and make an additional analysis of specific clusters

if required (Wu, J., Lin, Z., 2005).

2. 5.2 Customer Segmentation Model

The customer segmentation concept was built by American marketing expert,

Wendell R. Smith, in the middle of 1950s. "Customer segmentation refers to

classifying customers by their value, demands, preference and other factors in the

circumstances of clear organization strategies, business model and targeted market".

Customers in one group have definite similarities, whereas different segments of

customers have clear characteristics. Customer segmentation model is built by

classifying customers according to assured standards on selected segmentation

variables. There are two types of consumption-based customer segmentation models

(Wu, J., Lin, Z., 2005).

2. 5.3 RFM Model

RFM segmentation model is a model that distinguishes important customers

by three variables; customers consumption interval, frequency and spent money. R

symbolizes recency referring to the interval between the time when the latest

consuming behavior happens and present. How much the interval is shorter, the R is

bigger. F symbolizes frequency referring to the frequency of consuming behavior in

a period of time. M symbolizes monetary referring to consumption money amount

in a period of time. Researches show that the bigger the R and F values are, the

more likely the related customers are to make a new deal with ventures.

39

Furthermore, the bigger M is, the more likely the related customers are to react to

ventures’ products and service again (Wu, J., Lin, Z., 2005). RFM method is very

successful for customer segmentation. We can arrange customers by their

consuming date and then we put the most recent customer in front. Thus, customers

can be classified into some groups. Then, F and M are standardized and arranged in

the same way as mentioned before. At this time, each customer is placed in a three-

dimension space, related to a coordinate of (R, F, M). By calculating R*F*M, the

value of RFM for each customer can be achieved (Wu, J., Lin, Z., 2005). With these

RFMs arranged, the groups of customers can be classified consistent with certain

proportion. For example, to a commercial enterprise, customers whose RFM related

values are in the first 20 percent can be considered as their most valuable customers

(Wu, J., Lin, Z., 2005). It is essential to quantify customer behavior so that we can

analyze the short and long term outcome of our segmentation formulae. The purpose

of RFM is to give a simple framework for customer behavior analysis. Once

customers are allocated RFM behavior scores, they can be grouped into segments

and their consequent effectiveness analyzed. This effectiveness analysis then forms

the basis for future customer contact frequency decisions (Miglautsch, J.R., 2001).

There are some methods for RFM scoring in the literature which they are as follows.

2. 5.4 RFM Scoring

The purpose of RFM scoring is to plan future behavior (driving better

segmentation decisions). In order to allow planning, it is critical to interpret the

customer behavior into numbers which can be used through time (Miglautsch, J.R,

2001).

Too often, direct marketers will use static customer selections. When initially

building their segmentation system, they defined some factors with some thresholds.

If these thresholds keep fixed, the results will be poorer and poorer over time. It is

called bracket creep problem (Miglautsch, J.R, 2001). Some common scoring

methods are used to avoid this problem.

a. Customer Quintiles

The most common scoring method is to arrange customers in downward order

(best to worst). Customers are then divided into five equal groups or quintiles. The

best group receives a score of 5, the worst a 1. For Recency, customers are sorted by

days since last purchase, the lower the number of days, the better the score is. For

Frequency, customers are sorted by purchases number, the upper the number of

40

purchases, the better the score. And for Monetary, customers are sorted by the

amount of money spent. The upper the amount, the higher the score is. Each time

customers are scored, a new comparative segmentation scheme is built. This has the

benefit of quantifying customer behavior which can be projected into the future

(Miglautsch, J.R, 2001). The comparatively best customers would always fall into

the 5, 5, 5 category. It is essential to recognize where the cutoff points fall, since

they automatically change with each customer scoring. The customer quintile

method has the benefit of yielding equal numbers of customers in each segment.

There are five equal groups for RFM, generating 125 equal size segments in general.

Initial analysis would be to contact all customers, look at the performance of each

individual cell and understand how different segments of the customers carry out

(Miglautsch, J.R, 2001).

The customer quintile method does meet some scoring confronts in the area of

Frequency. In most direct marketing customer files, a high percentage of the

customers have only ordered once. This percentage is frequently as high as 30%-

60%. If more than 20% of the customers have only one purchase, then the lowest

Frequency group will have a purchase amount of 1. Since that group cannot keep all

the customers with only one purchase, some of them will be sorted into the 2 score

group. Their behavior is identical to those in the 1 score, they simply overflowed. If

40% of the customers had only one purchase, then both 1 and 2 score groups would

have equal behaviors. If the percentage ran as high as 60% (which is not that

unusual) then three of the five quintiles would have the equal behavior.

Remembering the reason of RFM, this would be a less than satisfying result. A

second concern with the quintile method is its relative sensitivity. At the high end of

our Frequency model customers average 7.4 purchases. That is significantly more

than the 1.0 purchases at the bottom and approximately twice as great as the 3.4

purchases in the 4 score group. However, the Paretto Principle (commonly called the

80/20 rule) still applies within the 5 score group. This means that there are a small

number of very large customers and a larger number of relatively smaller customers

who make up that 7.4 average (Miglautsch, J.R, 2001).

As long as our segmentation method is primarily built for mailing goals, this

difference is debatable. Certainly the 5 and 4 groups would be mailed. However, if

our RFM model is being used to make possible telemarketing or field sales contact,

extra sub-segments would be vital to identify the super customers. The customer

quintile scoring method produces some unsatisfying results at both the top and

bottom of the scale. It tends to group together customers who have hugely different

41

buying behavior (at the top) and subjectively break apart customers who have same

behavior (at the bottom) (Miglautsch, J.R, 2001).

b. Behavior Quintile Scoring

An alternative scoring method has been made by John Wirth. It also sorts

customers by behavior but, instead of building arbitrary cutoffs at an assured percent

of the customers, it produces cutoffs on percentage of behavior. This method

appears to defeat the sensitivity problems mentioned above. Five groups are still

produced, but monetary score would produce equal amounts of sales in each

quintile. Behavior scoring has the benefit of grouping customers by similar

behavior. Since segmentation decisions are based on precedent customer behavior,

this permits better segmentation (Miglautsch, J.R, 2001).

i. Frequency

The Behavior method does suffer from similar problems when beginning

Frequency score. If we initiate at the top of the Frequency sort and deduct each

customer’s frequency from total Frequency, the customers purchased only once may

not equal 20% of entire Frequency. In such a situation, some of the customers who

have bought twice will be included in the 1 score group with this method. It is also

worrying to sort customers from top to bottom in a computer generated scoring

system. A particular sort file must be created and each scoring process must be

accomplished uniquely. The Mean scoring method, an additional improvement of

the John Wirth method has been developed by Ted Miglautsch (Miglautsch, J.R,

2001). When scoring Frequency, the solitary purchasers are given a score of 1. The

system then averages the remaining Frequencies to find out the mean. If a customer

total falls below the mean, he will have the score of 2. This process is duplicated

two more times giving us quintiles of behavior which have sensitivity on both ends

of the scale and let scoring of many variables at the same time (Miglautsch, J.R,

2001).

ii. Recency

Because previous behavior is the best predictor of future behavior, Recency is

normally considered the most influential of the three variables. Recency plays an

important role in direct marketing decision making. Recent customers are

considered viable for a assured length of time. Unlike Frequency and Monetary,

customers reset themselves. At the center of Recency is the fact that most of the

42

customers fall into two groups: hot and dead. Although Recency can be scored by

sorting customers by days since last purchase, industry list meeting suggest a more

calendar based method. “Hotline names” normally represent purchasers within three

months or 90 days. Business-to-business direct marketers often lengthen these time

frames since their customers can stay viable even though individuals change

(Miglautsch, J.R, 2001).

c. Weighting

With relational, database-driven marketing databases becoming more ordinary,

most marketers can select RF&M scores separately. Though, others are not as lucky

and need a single field to do the work of all three variables. The benefit of a single

variable is that customers can simply be segmented by a single query on one field

(Miglautsch, J.R, 2001). Donald R. Libey, in his book "Libey on RFM", proposes

that Monetary, Frequency and Recency values can be added jointly (Miglautsch,

J.R, 2001). Scoring is not explicitly argued but he present a formula for creating a

single RFM value. His method contains adding average order and Frequency per

year. To improve this complex formula, marketers can multiply Rx3, Fx2 and Mx1.

This would give the best customers a composite score of 30 (5x3)+(5x2) +(5x1).

This not only gives more influence to the most recent names, it also gives a bit of a

boost to Frequency. The logic behind this, is that if two customers have the same

Recency, spent the equal amount but one purchased several times and the other only

once, the more frequent buyer is much more probable to react. One extra

enhancement is often employed in generating a complex score. Instead of

multiplying by 3, 2&1, alternate 9.9, 6.6 and 3.3. This produces a range of complex

scores between 99 and 19.8. It preserves the approximately 3x weighting of R; it

produces more of a 100 point scale (Miglautsch, J.R, 2001).

d. Life-to-Date

Generally, RFM scoring is stand on life to date totals. It is frequently requested

whether it would progress RFM scoring to shorten up the time frame. The idea is

that if Recency is so influential, maybe we should consider only the recent behavior

of the precedent few years; an excellent proposal but filled with danger. The basic

idea again is quantifying behavior for the point of customer segmentation. It is clear

that high RFM customers are easily recognized. The factual challenge is to

recognize viable customers further than the 12 month window in some areas like

direct marketing. Should any of them be mailed and marketed? Certainly some

43

should. To gain this wider viewpoint, it needs that all obtainable customer history be

examined (Miglautsch, J.R, 2001).

2.5.5 Customer Value Matrix Model

The Customer Value Matrix was made from a want to apply RFM to the

small-business retail environment. After some experiments with applying RFM in

small businesses, it became clear that RFM was too difficult and time-consuming

for them. The problem was that, while RFM was comparatively easy conceptually,

the consequential segmentation was often complex to understand and even more

difficult to use them. By three values per RFM variable, RFM analysis makes 27

customer segments. For RFM analysis to be useful, the marketer must know which

groups can be combined for a exacting strategy or tactic (Marcus, C., 1998).

Closer test of the RFM analysis emphasized the co-linearity of the Frequency

Purchase frequency and the total Monetary Value variables. An extra purchase by a

customer results an increase in the total monetary value of that customer. Given this

result, Charles Edmundson recommended using Average of Purchase Amount

instead of the total Monetary Value of a customer. By this, we eliminated the co-

linearity between the two variables. Besides, for the more clarity, the Purchase

Frequency variable was changed to Purchases number. These changes showed

refinements over usual RFM analysis; though, they did not determine the problem of

finishing up with too many segments to interpret and to work with (Marcus, C.,

1998). For solving this issue a simplified, more actionable version of RFM was

needed. In the first step, we centered on the two variables that best expressed the

value of a customer: Purchases Number and Average Purchase Amount. The third

variable, Recency, gives motivating information that can be joined with the two key

variables. We can also use other important variables such as Type of Purchase or

Length of Relationship. Using just Purchase Frequency and Average Purchase

Amount was piece of the answer; moreover it is needed to simplify the segmentation

to a 2 * 2 matrix. Matrices have been effectively used to help in the understanding

of information for decision-making reasons. Maybe the most usually known matrix

is Boston Consulting Group’s (BCG) Growth-Share Matrix centering on allocation

of resources given the market share place and growth potential of a given set of

business opportunities (Henderson, 1967; Porter, 1980, cited byMarcus, C., 1998).

The BCG Growth-Share Matrix can be used to market segments, products or still

countries. BCG’s Growth-Share Matrix segments business opportunities into

obviously defined groups (Cash cows, Stars, Dogs and Question marks). The use of

a comparatively straightforward method and easy-to-understand quadrant identifiers

44

has made the BCG Matrix an effective analytical tool. The BCG Matrix adds further

value by involving what managerial strategies and tactics are to be chased with each

business segment. Businesses that have high relative market share in low growth

markets (Cash cows) can be applied to support other developing businesses, as low-

relative-share businesses in low-growth markets are probable to be cash traps

(Dogs). Simplifying the RFM analysis to center on the customer-value-based

variables, Purchases Number and Average Purchase Amount, and applying a 2 * 2

matrix to correspond the resulting segmentation verified to be active in arriving at a

practical yet meaningful approach to customer segmentation (Marcus, C., 1998).

Customer value matrix model is an advanced model that is based on the traditional

RFM model. In this model, customer value matrix includes of the times of

purchasing (shown by F) and the average amount of purchasing (shown by A) (Wu,

J., Lin, Z., 2005). Average amount of purchasing replaces two variables in RFM

model between which there is multi-co linearity, which omits their linear result on

RFM model. In customer value matrix, the foundation value of F and A is their

average value correspondingly. Once the division of the axis is determined,

customers are located in one of the quadrants of the customer value matrix. By the

value of A and F, customers are categorized into four groups in the matrix, for

example customer who likes to consume (shown by I), customer who is important

for ventures (shown by II), customer who frequently consumes (shown by III), and

customer whose behavior is unsure to ventures (shown by IV). The consequence is

accessible in Figure 1(Wu, J., Lin, Z., 2005).

45

Chapter3: Research Methodology

Background of the study (Problem definition)

Research question Research objectives

Research motivation Research outline

46

3.1Research Methodology:

3.2Research Design: A research design is a roadmap for performing the marketing research project.

It gives details of each step in the marketing research project. Accomplishment of

the research design should result in all the information requested to construction or

solve the management-decision problem (Malhotra, K.N., 1996). Many designs

maybe are suitable for a given marketing research problem. A good research design

ensures that the information gathered will be related and useful to management and

that all of the necessary information will be achieved. A good design should also

assist to ensure that the marketing research project will be performed effectively and

efficiently (Malhotra, K.N., 1996). The research design of this study is illustrated in

figure 3.1. Detailed descriptions are explained below.

Figure 3.1: Research design of this study

3.3Research Purpose: According to (Malhotra, K.N., 1996), basic

47

research designs can be categorized in terms of the research objectives. They are of

two wide types of research: exploratory and conclusive. These types are explained

below.

Exploratory research is a research performed to explore the problem situation

to achieve ideas and insight into the problem facing the management or the

researcher (Malhotra, K.N., 1996).

A conclusive Research is designed to help the decision maker in determining,

assessing, and choosing the best manner of action for a given condition (Malhotra,

K.N., 1996). Conclusive research is in two types: Causal and Descriptive. Causal

research is a kind of conclusive research whose major goal is to gain evidence

concerning cause-and-effect (causal) relationships (Malhotra, K.N., 1996).

Descriptive research is a kind of research that has as its main goal the explanation of

something usually market features or functions. Descriptive research supposes that

the researcher has previous knowledge about the problem situation this is one of the

main differences between deceptive and exploratory research (Malhotra, K.N.,

1996). Among the main kinds of descriptive studies are internally or externally

centered sales studies, consumer perception and behavior studies, and market

characteristics studies. Additionally, descriptive research uses different verity of

data collection methods like secondary data analyzed quantitatively or surveys

(Malhotra, K.N., 1996).

The approach of our study is data mining. According to data mining definition

by (Han, J., & Kamber, M., 2006), data mining refers to mining" knowledge from

large amounts of data". When approaching a data-mining problem, a data-mining

analyst may already have some a priori hypotheses that he or she would like to test

concerning the relationships between the variables (Larose D.T., 2005). Though, all

the time, analysts do not have a priori notions of the expected relationships among

the variables. Particularly when faced with large unknown databases, analysts often

prefer to use exploratory data analysis (EDA) or graphical data analysis (Larose

D.T., 2005). Exploratory data analysis (EDA) let the analyst to explore the data set,

check the interrelationships among the attributes, recognize attractive subsets of the

observations, and develop an original idea of possible relations between the

attributes and the target variable, if any.

Data mining approaches are in two kinds: Descriptive and Predictive (Han, J.,

& Kamber, M., 2006). Predictive mining tasks make deduction on the current data

48

so as to make predictions (Han, J., & Kamber, M., 2006). Descriptive mining tasks

typify the general properties of the data in the database.

The focus of this study is data mining, which is an approach that combines

exploration and confirmatory analysis. So the purpose of this research is

exploratory. While, we try to understand customer behavior by building pattern by

data mining tools. According to definition of data mining approaches, the focus of

our data-mining task is descriptive.

3.4Research Approaches: There are two kinds of approaches for

research design: quantitative and qualitative (Malhotra, K.N., 1996). "Quantitative

research is an unstructured, exploratory research methodology based on small

samples that provides insights and understanding of the problem setting "(Malhotra,

K.N., 1996).

In contrast, Qualitative research is a methodology that searches to quantify the

data and usually applies some form of statistical analysis. The findings of this kind

of research can be treated as conclusive and applied to suggest a final course of

action (Malhotra, K.N., 1996). Descriptive researches frequently are quantitative

research (Malhotra, K.N., 1996). The concept of data mining allow decision maker

to be supported by qualitative descriptive research. The focus of this study is on

data mining so the research approach of this study is quantitative.

3.5Research Strategy: Research strategy will be a common plan of how

you are going to respond the research questions. It is a particular way to gather data

(Saunders et al, 2000).

A researcher based on the research question should choose among survey,

secondary data, case study, experiment or history (Yin, R.K, 1994). There are two

kinds of data generally used in researches: Primary data and Secondary data.

Primary data is produced by the researcher particularly to address the research

problem (Malhotra, K.N., 1996). Secondary data is data collected for some reason

other than problem at hand (Malhotra, K.N., 1996). It consists of information made

existing by business and government sources and computerized databases.

Secondary data are a reasonable and fast source of background information

(Malhotra, K.N., 1996). Two major categories are defined for secondary data:

Internal secondary data and External secondary data (Malhotra, K.N., 1996).

External secondary data create external to the organization (Malhotra, K.N.,

1996). Internal secondary data is data available within the organization for which

49

the research is being performed (Malhotra, K.N., 1996). While, it is possible that

internal secondary data may be accessible in practical form, it is more usual that

considerable processing effort will be required before such data can be applied

(Malhotra, K.N., 1996).

The focus of this study is data mining and the data has been collected from the

database of Kalleh Company, so the suitable strategy for this research is secondary

data which is internal. In the end, as the focus of this study is on data mining, the

purpose of this research is exploratory. The approach of this study is quantitative,

strategy of the research is secondary and internal data and the data mining approach

is descriptive.

3.6Research process: The purpose of this research is to understand changes

happening in the customer buying behavior during time. Figure 3.2 shows the

general overview of change mining flowchart.

Figure 3.2: Change mining process perspective

As it is shown, the input of this flowchart is RFM data which show the

customer purchasing behavior, some demographic variables and product data. This

data induced to the Change Miner. Change mining procedure consists of different

steps implemented by different data mining techniques and algorithms in each. In

chapter2, different studies related to change mining were reviewed. Based on

literature, change mining has several steps, includes describing customer behavior

by mining association rule and mining change pattern. Most of the works have been

done for retail marketing. The focus of this research is using change mining in

behavior of FMCG manufacturer and Distributor Company. It is to analyze the

customer behavior in two time snapshots and the output will be change patterns

happened during time periods.

In this thesis, the research process has been followed. The following process is

based on previous methodology on Change mining (Chen et al, 2005). In this study,

according to pervious works, different steps of change mining were studied and with

some changes integrated to a unique process.

The whole process of change mining is shown in figure 3.4. The process

Change Miner Change

patterns Input Data: RFM,

Demographic, Product

consists of several steps such

Segmentation, Mining Customer Behavior,

Each step by itself consists of several

illustrated.

Figure 3.4: Change mining

As can be seen in figure 3.

customer behavior. In order to implementing

In this study, we use SQL server 2000 for data

market segmentation. Also for building customer behavior pattern, we use R Open

source language Programming

the methods have been used, explained in detail.

3.7Data Collection and question and determining the

data to connect to the research question

strategy, empirical data are

Primary data is produced by the researcher

problem (Malhotra, K.N.,

Data CollectionData Pre

Processing

Data Collection

Data Pre-Processing

• Data Cleaning

• Data Transformation

• RFM variables

steps such as Data Collection, Data Pre-Processing, Customer

Mining Customer Behavior, and Change

Figure 3.3: Change mining process

Each step by itself consists of several tasks. In figure 3.4 the detail of each step is

Figure 3.4: Change mining process in detail

As can be seen in figure 3.4, there are various steps in mining changes in

customer behavior. In order to implementing this method, programming required.

In this study, we use SQL server 2000 for data preprocessing like building


source language Programming (R Software, 2007). In the next section, each step and

the methods have been used, explained in detail.

3.7Data Collection and Description: After defining the research

question and determining the appropriate research strategy, we should determine the

data to connect to the research question (Yin, R.K, 1994). As mentioned in research

strategy, empirical data are usually in two types: primary data and secondary data.

d by the researcher specially to deal with the research

1996).

Data Pre-Processing

Customer Segmentation

Mining Customer Behavior

Processing

Data Cleaning

Transformation

RFM variables

Cstomer Segmentation

• Building Customer Value Matrix

• Segment Customers

Mining Customer Behavior

• Mining Association Rules in each time snapshot

50

Processing, Customer

Mining.

the detail of each step is

, there are various steps in mining changes in

, programming required.

building RFM,


. In the next section, each step and

defining the research

research strategy, we should determine the

, 1994). As mentioned in research

nd secondary data.

the research

Change Mining

Change Mining

• Rule-Matching: computing similarity & difference measure

• Mining change pattern includes Emerging pattern, added/perished rule and unexpected pattern

51

Secondary data is data gathered for some reason other than current problem

(Malhotra, K.N., 1996). It consists of information prepared by business and

government resources and computerized databases. Secondary data can classify to

two types, internal and external data (Malhotra, K.N., 1996).

For data mining purposes secondary data used mostly. Change mining

researches mostly work with secondary data collected in business databases. Hence,

this study is based on secondary data were gathered from Kalleh company which is

a manufacturer and distributor of food products in Iran market. Here we bring a

brief history of Kalleh Company.

This data is purchasing transaction data of Kalleh customers like fast-foods,

restaurants and coffee-shops which buy different categories of product from this

company. We Saved data in SQL 2000 (SQL Server, 2000). According to (Chen et

al, 2005), data for change mining includes 3 categories:

Customer data: for market segmentation and mining customer behavior one

kind of variables needed is demographic data. In this study, based on the collected

data, we have one demographic variable which shows the geographic area of each

customer. Beside, there was another variable, customer type that because of missing

value, it couldn’t provide any value for us, which we remove them.

Product data: showing different products provided for customers. In our data

we have about 800 product and 13 different product categories which are shown in

fig3.5

Purchasing transaction data of customers: usually, some valuable variables are

hidden in large quantity of raw data and can be achieved by data integration and

transformation. Customer behavioral variables like Recency, Frequency and

Monetary are unknown in customer and transaction database. They can be taken out

from these data (Chen et al, 2005). In this study, RFM variables used to analyze

customer purchasing behavior during two time snapshots.

The data gathered from 2 years of purchasing transaction while the number of

kalleh customers in the market of restaurants and fast-foods of Tehran during this

period is about 2457. Table3.1 shows the information data gathered from Kalleh

company database. This information gathered from Kalleh database based on the

literature and expert opinions.

Table3.1: Data collected from Kalleh Company

Figure 3.5: Product categories of Kalleh

For this study 3 types of data were needed

data for extracting RFM, customer data and product data. RFM variables which are

the input of change mining process which are extracted from purchasing transaction

data to analyze the customer behavior. Besides, customer geographic data and

product data extracted from Kalleh database.

Customer Data

• Geographic Area of each customer

Product Categories

Pizza Cheese

Cooking Cheese

Processed Cheese

Tehran meat products

Amol Meat products

Freezed meet product

Yogurt-Milk

Other Dairy products

Ice Creams

sauces

Dishes

Other complementory products

Table3.1: Data collected from Kalleh Company

Figure 3.5: Product categories of Kalleh Company

For this study 3 types of data were needed includes, purchasing transaction



the customer behavior. Besides, customer geographic data and

product data extracted from Kalleh database.

Product Data

• Product Code

• Product Categroy

Transaction Data

• Data of purchase

• Purchase amount: price

• Product purchased

• number of orders for each product

Product Categories

Pizza Cheese

Cooking Cheese

Processed Cheese

Tehran meat products -not freezing

Amol Meat products-not freezing

Freezed meet product

Milk-drinking Yogurt

Other Dairy products

Ice Creams

Other complementory products

52

, purchasing transaction



the customer behavior. Besides, customer geographic data and

Purchase amount:

53

3.8 Data Pre-Processing: Much of the raw data included in databases is un-preprocessed, imperfect, and

noisy. For instance, the databases may include fields that are obsolete or

unnecessary, missing values Outliers, Data in a form not appropriate for data mining

models, values not steady with policy or common sense. The databases require

undergoing preprocessing, in the form of data cleaning and data transformation to be

practical for data mining reasons (Larose, D.T., 2005).

Data cleaning and Integration:

Prior to analysis, data accuracy and consistency must be guaranteed to gain

correct results (Chen et al, 2005). Real-world data mostly are unfinished, noisy, and

unpredictable. Data cleaning processes effort to fill in missing values reduce noise

while recognizing outliers, and accurate inconsistencies in the data (Han, J., &

Kamber, M., 2006).

Noisy Data:

The data saved in a database may reveal noise, exceptional cases, or imperfect

data objects. When mining data regularities these objects may mystify the process.

Consequently, the correctness of the discovered patterns can be poor. So it should be

regarded as to handle these noises and exceptional cases (Han, J., & Kamber, M.,

2006). In our database, it the customer base and purchasing transactions, we have

some customers that belong to the Kalleh. These are noises that we have in our

database which remove all of them by their IDs from database.

Missing values:

When you have some record that some of these attributes have no value called

missing value. There are several methods to fill in missing values such as ignoring

the record, filling in the missing value manually which is time-consuming and may

not be feasible given a large data set with many missing values, using a global

constant to fill in the missing value and replace all missing attribute values by the

same constant, such as a label like Unknown" or using the attribute mean to fill in

the missing value or some other ways that exist in literature (Han, J., & Kamber, M.,

2006).

It is important to note that, in some cases, a missing value may not involve an

54

error in the data. Software routines may also be applied to expose other null values.

Therefore, though we can try our best to clean the data after it is gathered, good

design of databases and data entry accuracy; procedures should help reduce the

number of missing values or errors in the first place (Han, J., & Kamber, M., 2006).

In this study, excepting one variable, the design of the database for sales

transaction are sensitive to null values in data entry moments which help to

minimize the missing values. One variable that has missing value is customer type

that it doesn’t sensitive to null value in data entry moments in database and we faced

a huge number of null values. Hence, this variable could not create any value for us;

we remove it from our work.

Data Transformation:

When the data are transformed or consolidated into forms appropriate for

mining, it is called data transformation. Data transformation can include the

following Tasks (Han, J., & Kamber, M., 2006).

Where summary or aggregation operations are used to the data, it is called

Aggregation. For instance, the daily sales data may be aggregated, to calculate

monthly and yearly total amounts. This step is normally used in building a data cube

for analysis of the data at multiple granularities (Han, J., & Kamber, M., 2006). In

this study, invoices sales data aggregated to compute the average sales per each

period and average of frequency of purchases. In addition, we use aggregation for

calculating the average purchases for each customer and total number of purchases.

Another data transformation task is Data Generalization, where low-level or

primitive (raw) data are substituted by higher-level concepts during the use of

concept hierarchies. In this study, we have about 800 products in 13 categories. We

replace products by categories and then replaced by super-categories according to

the experts opinion for mining purposes. Attribute construction (or feature

construction), where new attributes are built and added from the given set of

attributes to assist the mining process (Han, J., & Kamber, M., 2006).

Generally, some useful variables can be unknown in a large quantity of raw

data, and therefore can be gained through data integration and transformation (Chen

et al, 2005). In this study, we use customer behavioral variables (Recency,

Frequency and monetary) for customer segmentation. These variables are hidden in

customer and transaction databases, and can be extracted from data integration and

55

transformation (Chen et al, 2005). (Stone, 1995 cited by Chen et al, 2005)

mentioned that "recency is the interval between the most recent transaction time of

individual customers and evaluation time". In this study, we consider the evaluation

time the next day of the end of each period. Frequency shows the average

expenditure of a customer during a period (Chen et al, 2005). Finally, frequency

shows the number of purchases in each period for each customer. In this study, we

calculate number of purchases for each customer as frequency, average sales for

each customer in period and the interval between the last purchase and the day after

the last date of each period.

Data Discretization:

Since the data needed for analyzing association rules must be discrete,

continuous variables should be altered to discrete type. Discrete values have

significant roles in data mining and knowledge discovery. They are about intervals

of numbers which are more concise to represent and specify, easier to use and

comprehend as they are closer to aknowledge -level representation than continuous

values. Many studies represent induction tasks can profit from discretization

because rules with discrete values are usually shorter and more understandable and

discretization can lead to advanced predictive accuracy. As well, mining in a

reduced data set need fewer input/output operation and is more efficient then a

larger and un-generalized data set. Because of these benefits, discretization

techniques are used previous to data mining as a preprocessing task. Discretization

technique can be classified based on how the discretization is done, such as whether

it uses class information or which direction it proceeds. If the Discretization process

employs class information, then it is said supervised Discretization, or else, it is

unsupervised. If the process begins by first result one or a few points called cut

points to split the whole attribute range, and then duplicates this recursively on the

resulting intervals, it is called top down Discretization or splitting. This difference

with bottom-up Discretization or merging starting by considering all of continuous

values as potential split-points, takes out some by merging neighborhood values to

form intervals, and then recursively applies this process to the resulting intervals

(Han, J., & Kamber, M., 2006).

Discretization can be done recursively on an attribute to give a hierarchical or

multi-resolution partitioning of the attribute values, recognized as concept hierarchy.

Concept hierarchies are helpful for mining at multiple level of abstraction. Though

data is lost by these generalizations with data reduction by collecting and replacing

56

low-level concepts by high-level concepts, the generalized data may be more

significant and easier to interpret (Han, J., & Kamber, M., 2006). There are

numerous discretization methods available in the literature based on different

definitions mentioned above.

In this study, the discretization method used, is binning. Binning is the

simplest method discretizing a continuous-valued attribute by producing a particular

number of bins. The bins can be produced by equal-width and equal-frequency (Liu

et al, 2002). In equal-width, the continuous range of a feature is evenly separated

into intervals that have an equal-width and each interval represents a bin. In equal-

frequency, an equal number of continuous values are set in each bin (Liu,

2002).these methods are responsive for a given number of bins. In this study, based

on domain experts' opinions, for each variable, we discretize them by equal

frequency method.

3.9Customer Segmentation: There are many analytics methods which

applied for market segmentation. One of the most traditional approaches of market

segmentation is demographic segmentation. The other methods have also use buyer

attitudes, motivation an attitudes and pattern of usage. Companies that capture

customer and purchase information apply such information to analyze customer

behavior for their marketing efforts (Marcus, C., 1998).

While the availability of customer purchase information has permitted

marketers to develop richer, more complicated customer segmentation schemes,

simplicity has also proven its place. For many years, RFM (recency, frequency and

monetary value) has been applied to segment customers to assist marketers

optimizing their marketing efforts. Many times, RFM has been confronted by

innovative conceptual approaches prepared possible by new technologies such as

neural networks. Yet, many marketing tasks continue to count on RFM variables,

particularly direct marketing because the lift experienced using alternative methods

does not normally guarantee the costs of implementing those methods. There are

costs linked with improved technical complexity, particularly that of taking the

analysis away from marketers and putting it into the hands of programmers and

statisticians. Besides the costs of explanation and communication – as marketers

require to develop actionable strategic and tactical decisions from the research

findings are important. The Customer Value Matrix is a customer segmentation

technique that is simple yet, powerful approach overcoming the above limitations.

Its effectiveness lies not only in that it recognizes key customer segments, but also

57

in that, it emphasizes appropriate marketing strategies and tactics in a manner that

can be eagerly communicated and easily executed (Marcus, C., 1998).

3.9.1 Customer Value Matrix The Customer Value Matrix was developed

from a desire to apply RFM to the small-business retail environment, but it became

clear that RFM was too complex and time-consuming for marketers. There were

some problems, which are as follows: while RFM was comparatively simple

conceptually; because of producing too many segments, the consequential

segmentation was often difficult to understand and even more difficult to apply.

Additionally, Closer test of the RFM analysis highlighted the co-linearity of the

Frequency of Purchase and the total Monetary Value variables. (Charles Edmundson

cited by Marcus, C., 1998) recommended using Average Purchase Amount as an

alternative of the total Monetary Value of a customer to eliminate the co-linearity

between the two variables. In addition, for greater precision, the variable Frequency

of Purchase was transformed to Number of Purchases. These changes showed

refinements over usual RFM analysis; however, they did not solve the problem of

ending up with too many segments to understand and to work with (Marcus, C.,

1998).

What was required was a simplified, more practical version of RFM. First,

centered on the two variables that best explained the value of a customer: Number of

Purchases and Average Purchase Amount and the other was simplifying the

segmentation to a 2*2 matrix (Marcus, C., 1998).

3.9.2 An effective analytical tool

Matrices have been effectively applied to help in the information

understanding for decision-making goals. Maybe the most usually known matrix is

Boston Consulting Group’s (BCG) Growth-Share Matrix, which centers on

allocation of resources specified the market share position and growth potential of a

given set of business opportunities (Henderson, 1967; Porter, 1980 cited by Marcus,

C., 1998 ). The BCG Growth-Share Matrix can be used for segmenting markets and

products. BCG’s Growth-Share Matrix segments business opportunities into four

obviously described groups (Cash cows, Stars, Dogs and Question marks). The BCG

Matrix adds additional value by involving what managerial strategies and tactics are

needed with every business segment. The application of a comparatively simple

scheme and easy-to-understand quadrant identifiers has made the BCG Matrix an

effective analytical tool (Marcus, C., 1998).

58

Simplifying the RFM analysis to center on the customer-value-based variables,

Number of Purchases and Average Purchase Amount, and using a 2*2 matrix to

correspond the resulting segmentation proved to be active in arriving at a realistic

and meaningful approach to customer segmentation (Marcus, C., 1998). In this

study, according to literature and based on expert opinions, we have chosen

Customer Value Matrix to segment customer behavior.

3.9.3 Customer Value Matrix Methodology Customer Value Matrix building

has some steps. In the first step, we require some basic customer and purchase

information to involve in a relatively simple methodology. In the second step, the

segmentation process executes to allocate each customer in the Customer Value

Matrix. Finally, we should obtain four segments with key differences among the

resulting customer segments (Marcus, C., 1998).

Data:

The data requested to develop the Customer Value Matrix are customer

identification (ID) number, the purchase date and the total purchase amount. The

customer ID number is applied to finding out the purchases of each customer. The

total Number of Purchases is basically a count of the unique dates for a given

customer’s invoices. The total amount of each purchase is applied to calculate the

Average Purchase Amount (Marcus, C., 1998). In this study, the data that we have

to build Customer Value Matrix is customer identification number, the date of each

purchase and the total amount of each purchase.

Segmentation:

The segmentation process using the Customer Value Matrix needs the

computation of the average values for the Number of Purchases and Average

Amount Spent. The average value for the x-axis, or Average Number of Purchases,

are considered by taking the total number of purchases for the customer base and

splitting it by the total number of customers in database. The average value for the

y-axis, or Average Purchase Amount, is obtained by taking the total revenue and

splitting it by the total number of purchases. The axes’ averages then provide to

separate the high and low values on each scale. In this study, according to gathered

data, we could not calculate the revenue, so instead of revenue we put total sales.

Table 3.2 shows these variables and their calculation for this study. You can see

result in chapter4.

59

Table3.2: calculating variables for customer value matrix

Then, we compare each customer’s Average Number of Purchases and

Average Purchase Amount to the gained average values for the whole customer

base. So, each customer is allocated exclusively to one of the four segments based

on whether they are above or below the axis averages. The output of this step is a

matrix as illustrated in figure 3.5.

Figure 3.6: customer value matrix

You can see the result of this step in next chapter.

Frequency Avg.

Frequency

Monetary

Av

g.

Mo

net

ary

Spender

Best

Uncertain

Frequency

60

Customer Value Matrix centers on the Number of Purchases and the Average

Purchase Amount as the primary variables, as best representation of the customer

value. Using the Customer Value Matrix as the foundation, any number of variables

(like geographic, demographic, the purchase recency or the customer relationship

length) may be overlaid on the segmentation to get more detail according to the

customer data and their transactions.

The methodology for the development of the Customer Value Matrix shows

that relatively simple yet effective customer segmentation is indeed possible. In this

study, according to the literature review and by considering expert opinion, we

segmented our customers based on customer value matrix by (Marcus, C., 1998).

Customer Value Matrix focuses on the Average Purchase Amount and the Number

of Purchases as the primary variables, which are best portray of customer value. By

this method we have four segments which they can be differentiated. The result can

be found in chapter4.

3.10 Mining Customer Behavior: Different methods to describe

customer behavior exist in the literature. Among them, there are various types of

conjunctive rules to build customer behavior pattern including association rules and

classification rules (Agrawal et al. cited by Adomavicius, G. Tuzhilin, A., 2001).

Using rules to describe customer behavior has certain advantages. Besides being an

intuitive and descriptive way to represent behaviors, a conjunctive rule is a well-

studied concept used extensively in data mining, expert systems, logic

programming, and many other areas. In addition, researchers have proposed many

rule discovery algorithms in the literature, especially for association rules

(Adomavicius, G. Tuzhilin, A., 2001). To discover rules that describe the behavior

of customers, we can use various data mining algorithms, like Apriori for

association rule mining.

Association rules were originally used to analyze the relationships of product

items bought by customers at retail stores (Agrawal, Imielinski, & Swami, 1993;

Srikant, Vu, & Agrawal, 1997 cited by Chen et al, 2005). In a customer behavior

research, association rule can be used to find the correlations between customer

profiles shown by demographic variables and purchased product by exploring

customer and product databases (Song et al, 2001). In this research based on the

literature, we mine customer purchasing behavior by association rule.

3.10.1 Association Rule Mining: A classic and normal association rule has

61

an implication of the form A->B, where A is an itemset and B is an itemset that

includes only a single atomic condition (Song et al, 2001). A and B are statements

regarding the values of attributes of an example in a database (Song et al, 2001). "A

is termed the left-hand-side (LHS), and is the conditional part of an association rule.

Meanwhile, B called the right-hand-side (RHS), and is the consequent part". A and

B are frequent itemset, If the relative support of an itemset satisfies a pre-specified

minimum support threshold (Chen et al, 2005).

The support of an association rule is the percentage of records containing

itemset A and B at the same time. The confidence of an association rule is the

percentage of records including itemset A that also include itemset B. the support

shows the usefulness of the revealed rule and the confidence signifies certainty of

the found association rule (Song et al, 2001). The most usual use of association rules

is market basket analysis, in which the market basket contains the set of items

(namely itemset) purchased by a customer during a single store visit (Chen et al,

2005). Association rule mining discovers all collections of items in a database

whose confidence and support meet or go above a pre-specified threshold value

(Song et al, 2001).

In this research we use the Apriori algorithm that introduced by (Agrawal et al,

1993) to build profile association rule. In the next section, we explain about Apriori

algorithm and the way it works.

3.10.2 Apriori algorithm: Apriori algorithm is one of the common techniques

used to find association rules (Agrawal et al, 1993). The name of the algorithm is

based on the fact that the algorithm uses prior knowledge of frequent itemset

properties. Apriori uses an iterative approach recognized as a level-wise search,

where k-itemsets are used to explore (k + 1)-itemsets.

First, the set of frequent 1-itemsets is found by scanning the database to

accumulate the count for each item, and gathering those items that assure minimum

support. The consequential set is indicated L1. Next, L1 is used to find L2, the set of

frequent 2-itemsets, which is used to find L3, and so on, until no more frequent k-

itemsets can be found. The result of each Lk needs one full scan of the database. To

advance the efficiency of the level-wise generation of frequent itemsets, an

important property called the Apriori property is used to reduce the search space. It

means that all nonempty subsets of a frequent itemset must also be frequent. By

definition, if an itemset I does not satisfy the minimum support threshold, then I is

62

not frequent, that is, P(I) < min sup. If an item A is added to the itemset I, then the

resulting itemset (i.e., I � A) cannot happen more frequently than I. Therefore, I �

A is not frequent either, that is, P(I � A) < min sup. Apriori property used in the

algorithm has two steps consisting of join and prune actions to make it more

efficient. A major challenge in mining frequent itemsets from a large data set is the

fact that such mining often produces a huge number of itemsets satisfying the

minimum support (min sup) threshold, especially when min sup is set low (Han, J.,

& Kamber, M., 2006). To overcome this difficulty, two concepts of closed frequent

itemset and maximal frequent itemset have been introduced. An itemset X is closed

in a data set S if there exists no proper super-itemset Y such that Y has the same

support count as X in S. An itemset X is a closed frequent itemset in set S if X is

both closed and frequent in set S. An itemset X is a maximal frequent itemset (or

max-itemset) in set S if X is frequent, and there exists no super-itemset Y such that

X � Y and Y is frequent in S(Han, J., & Kamber, M., 2006).

Once the frequent itemsets from transactions in a database D have been found,

it is simple to produce strong association rules from them (where strong association

rules satisfy both minimum support and minimum confidence). This can be done

using Equation (1-4) for confidence, which we show again here for completeness:

�� |�� _�� _�� , Equation (1-4)

The conditional probability is expressed in terms of itemset support count,

where support countA � B� is the number of transactions containing the itemsets A � B�, and support count(A) is the number of transactions containing the itemset

A.

So, association rules can be generated as follows:

• For each frequent itemset l, generate all nonempty subsets of l.

"s�l‐s�" �� _��!��_�� " min _��, • For every nonempty subset s of l, output the rule

, where min confis the minimum confidence threshold (Han & Kamber ,

2006).

63

In this research, the customer behavioral variables (RFM) and geographic

variable are associated with purchased products to build customer purchasing

behavior patterns. The association rules discovered at two periods of time to adopt

for change mining to identify customer behaviors that change over time.

We have applied Apriori algorithm by maximal frequent itemset to build

association between customer attributes and their purchased products. These rules

can include any number of attributes on either side of the rule. In the left hand side

(LHS) or conditional part of the rule, we have RFM and customer data and in the

right hand side (RHS) or consequent part, we have purchased product items. Not

all association rules are interesting to decision makers. Rule support and confidence

are two measures of rule interestingness and an interesting rule must satisfy the

minimum support and confidence determined by domain experts.

3.11 Change Mining: After building customer behavior patterns, we want to mine changes happened

in customer purchasing behavior. In this study, two measures of similarity and

unexpectedness from (Chen et al, 2005) are applied to investigate changes in

customer behavior. Also we have applied ordered variables information in

calculating these two measures which resulted in more knowledge of changes which

didn’t done in the previous works. First, we explain about change pattern and then

mathematically explain about the measures.

3.11.1 Change Mining:

After building customer behavior patterns, we want to mine changes happened

in customer purchasing behavior. In this study, two measures of similarity and

unexpectedness from (Chen et al, 2005) are applied to investigate changes in

customer behavior. Also we have applied ordered variables information in

calculating these two measures which resulted in more knowledge in found changes

which didn’t done in the previous works.

First, we explain about change pattern and then mathematically explain about

the measures.

Change Patterns:

Based on previous studies, four patterns are identified to measure changes in

64

customer behavior (Dong, G., & Li, J., 1999; Liu & Hsu, 1996; Padmanabhan &

Tuzhilin, 1999; Song et al, 2001 cited on Chen et al, 2005). These patterns include

emerging pattern, added pattern, perished pattern, and unexpected change. These

four change patterns are explained below.

Emerging patterns:

Emerging patterns is a pattern kind for knowledge discovery from databases.

It is described as rules or itemsets whose supports increase significantly between

time-stamped datasets (Dong, G., & Li, J., 1999). Emerging patterns can capture

emerging trends in time-stamped database or practical contrast between data classed

(Dong, G., & Li, J., 1999). In marketing management, emerging patterns involve

the same consumer behavior that exists in different periods of time with trend. The

positive pattern growth rate (i.e. the support of a rule increases over time) indicates

that the customer behavior becomes stronger over time. Meanwhile, a pattern

growth rate below zero specifies that the customer behavior is getting weak. For

emerging patterns, the conditional and consequent parts are the same for two rules,

but support for the two rules changes significantly between different time periods

(Chen et al, 2005). Emerging pattern have been proven practical as (Dong, G., & Li,

J., 1999) mentioned and (Dong, G., & Li, J., 1999) believed that EPs with low to

medium support, such as 1% to 20% can give useful new insights and assistance to

experts.

Added/Perished patterns:

Added pattern defined as a rule at period t' , if all conditional and consequent

parts differ significantly from any rule, at period t( . Perished Pattern is a rule at

periodt(, if all conditional and consequent parts differ significantly from any rule, at

period t' . A perished pattern is a vanished pattern found in the past but not the

present. The rule matching threshold (RMT) is applied by (Chen at al, 2005) to

measure the degree of change.

Unexpected change:

There are some works on unexpected changes in literature on mining

interesting (Chen et al, 2005). Liu and Hsu (1996) classified unexpected changes

into unexpected conditional changes and unexpected consequent changes. If the

conditional parts of �)*(and �+*' are similar, but their consequent parts are diverse,

then �+*' is an unexpected consequent change regarding �)*( (Liu & Hsu, 1996; Song

65

et al, 2001). furthermore, if the consequent parts of �)*( and �+*' are similar, but their

conditional parts are different, then �+*' is an unexpected conditional change with

respect to �)*( . In this research, the unexpected changes of customer behavior can

be identified in the form of unexpected purchasing (consequent) patterns and

customer shifting (conditional) patterns based on (Chen et al, 2005). After

explaining the four customer changes, this study elaborates on the measures used to

detect these changes.

Change Measure:

For calculating changes, we have two measures from (Chen et al, 2005), one is

similarity which calculate similarity percentage between two rules and the

unexpectedness measure applying when two rules don’t have any similarity to

discover any unexpected event in rules, either in conditional part or consequent part.

Before calculating these two measures, we need some notations that defined

below.

,*- �. �� /��/�� !� �� (; ,*1 �. �� /��/�� !� �� '; �)*- /� /��/�� ! �� ,*- , �)*- 2 ,*-; �+*1 /� /��/�� ! �� ,*1 , �+*1 2 ,*1; �)+ �. �� /��3�� ./� ��4�!�/��!5 /��/� �� /! �/�� 678�� )*- /�� *1 ; 9�)+9 �. ��43� �� /��3�� )+ ; )+ �. �� /��3�� ./� ��4�!�/��!5 /��/� �� :�� /�� ,78�� )*- /�� *1; 9 )+9 �. ��43� �� /��3�� )+ ; 9;)*-9 �. ��43� �� /��3�� 678 �� )*- ; 9;+*19 �. ��43� �� /��3�� 678 �� +*1 ; 9<)*-9 �. ��43� �� /��3�� ,78 �� )*-;

66

9<+*19 �. ��43� �� /��3�� ,78 �� +*1 ;

ℓ)+ �. ��4�!/��5 �� /��3�� 678 �� )*- /�� +*1 ; .)+ �. ��4�!/��5 �� /��3�� ,78 �� )*- /�� +*1 ; >)+? / 3��/�5 @/��/3!, A.� >)+? � 1,�� . �th /��3�� )+ ./� �. �/4 @/!� �� )*- /�� +*1 , /�� .�A�� >)+? �0, � � 1,2, … , 9�)+9; .)+ �. ��4�!/��5 �� /��3�� ,78 �� )*- /�� +*1; G)+H / 3��/�5 @/��/3!, A.� G)+H � 1,�� . :th /��3�� )+ ./� �. �/4 @/!� �� )*- /�� +*1 , /�� .�A�� G)+H� 0, : � 1,2, … , 9 )+9; 8)+ / 4/�� . ��4�!/��5 3�A� �)*- /�� +*1; 8)*- �. 4/I�4�4 ��4�!/��5 �� )*-; 8+*1 �. 4/I�4�4 ��4�!/��5 �� +*1; J)+ / 4/�� I�� 3�A� �)*- /�� +*1 ; JK)+ /� /�L�� 4/�� I�� 3�A� �)*- /�� +*1 ; M)+ / 3��/�5 @/��/3!, M)+ � 1, �� 4/IN8)*-, 8+*1O � 1; ��.�A��, M)+ � 0 In this study, first, we applied two measures of similarity and unexpectedness by

(Chen et al, 2005). The Similarity measure can be used to measure the degree of

likeness between two rules, and unexpectedness measure can be used to identify the

disparity between dissimilar rules. Two measures shown below:

8)+ � PℓQR ∑ TQRUU9VQR9 W XQR ∑ YQRZZ9[QR9 , �� 9�)+9 \ 0, /��9 )+9 \ 00 �� 9�)+9 � 0, ��9 )+9 � 0 ]

Where ℓ)+ and .)+ are defined as follows:

67

ℓ)+ � 9VQR9^_` abQc-a,abRc1a� 2

.)+ � 9[QR9^_` adQc-a,adRc1a� 3

In Eqs. (2) and (3), ℓfgand hfg represent the similarity of the conditional and

consequent parts, respectively. The degree of similarity,Sfg, is between 0 and 1,

where 0 indicates that the two patterns are completely dissimilar, and 1 indicates

that the two patterns are identical.

For mining change, we have some steps that are as below:

First, we calculate similarity measure for every rule in the first period to all of

the rules from the second period and vice versa.

Following calculating the similarity of patterns, the maximum similarity

degrees of Rules rfj( and rgj' are determined to measure the change of patterns

during periods t( andt'. The maximum degrees of similarity are represented using

Eqs. (4) and (5), as below.

8)*- � max 8)(*- , 8)'*- , … , 8)amRc1a*- � 4

8+*1 � max 8(+*1 , 8'+*1 , … , 8amQc-a+*1 � 5

According to (Chen et al, 2005), the maximum similarity provides the basis for

differentiating emerging patterns, added patterns, and perished patterns during

various periods. If the maximum similarity of Rule rfj( , Sfj- , equals 1 (or Sgj1 equals

1), then the rule exists in both time periods t1 and t2, and thus shows an emerging

pattern. If a rule displays positive growth (Sup2>Sup1), then the rule represents a

pattern of customer behavior that becomes strong with time. Vice versa, a growth

rate below zero indicates negative trend of customer behavior change.

If the maximum similarity of Rule rfj( , Sfj- , lies between 0 and 1, the two

rules share a partial resemblance. The decision maker determines a rule matching

68

threshold (RMT) to judge whether the similarity of a specific rule satisfies the

criteria set by the individual user. If the maximum similarity of Rule rfj(, Sfj-, is

smaller than RMT (Sfj- n ,;o� this rule gradually perishes in time period t', and is

therefore considered a perished pattern. Else, the rule will be not perished.

Meanwhile, if the maximum similarity of Rule rgj' is belowRMTSgj1 n ,;o�, rgj'

in period t' is quite different to the rules in periodt(, and thus it is considered an

added pattern, else it will be not added rule.

If the maximum value of similarity measure for one rule becomes 0, then

unexpectedness measure is used to judge whether the two rules consist of

unexpected changes. Unexpectedness was initially used as a subjective measure for

interestingness of pattern. Patterns are interesting if they are ‘surprising’ to the user

(Silberschatz, A., & & Tuzhilin, A., 1996)

In this study we have used the unexpectedness measure, introduced by (Chen

et al, 2005).

The measure is illustrated in equation (6)

J)+ � PℓQR ∑ TQRUU9VQR9 s XQR ∑ YQRZZ9[QR9 , �� 9�)+9 \ 0, /��9 )+9 \ 00 �� 9�)+9 � 0, ��9 )+9 � 0 ]

6

If δfg u 0, then Rule rgj' is an unexpected purchasing rule (i.e. unexpected

consequent change) according to rfj(. In this case, customers with same

characteristics shift their purchasing behavior or buy diverse products. If δfg n 0,

then Rule rgj' is an unexpected shifting rule (i.e. unexpected conditional changes)

according to rfj( . This change specifies that the customer group of specific products

has changed to another group. If the unexpectedness value equals 0, the two rules

are completely different. If the value of unexpectedness in comparison of a rule

from t( and rule of period t' become 0, then this is an unexpected perished. In

addition, vice versa, it is an unexpected added.

Our contribution:

69

In the previous work, two measures did not use the information that ordinal

data have by themselves. In this study, After mining changes by two measure

introduced by (Chen et al, 2005), we modified previous measures to mine changes

which use the information that ordinal numbers have by themselves.

Here we want to compare ordinal values instead of binary values for each

attribute. For doing so, we calculate distances between values of each common

attribute of LHS and RHS. According to (Han, J., & Kamber, M., 2006), the

dissimilarity (or similarity) between the objects described by interval-scaled

variables is typically computed based on the distance between each pair of objects.

One of the most popular distance measures is Manhattan (or city block) distance,

which is defined as:

��, L� � 9I)( s I+(9 v 9I)' s I+'9 v w v 9I)x s I+x9. The measures of similarity and unexpectedness are modified by using

Manhattan distances. The modified measures are as follow.

z)+? � The distance between pth attribute of �)*( and �+*'where the pth attribute is in

common in �)+ , this is based on the definition of Manhattan distance.

β)+H � The distance between pth attribute of �)*( and �+*' where the qth attribute is in

common in )+, this is based on the definition of Manhattan distance.

8|�L � |}~�|�∑ �~��^_` a�~�-a,a��1a� W |�~�|��QRZ^_` a�~�-a,a��1a� �|�L � |}~�|�∑ �~��^_` a�~�-a,a��1a� s |�~�|��QRZ^_` a�~�-a,a��1a� By defining these measures we bring the information from the ordinal data that we

have.

70

Chapter4: Results & Analysis

Data preprocessing result

Customer Segmentation result

Mining customer behavior result

Change mining result

The data pre-processing phase of analysis has been done in SQL server 2000

(SQL Server, 2000) and the data

software, 2007). This chapter shows the analysis and result of each step in change

mining process.

4.1 Data preprocessing result4.1.1 Data Cleaning

which are the customers who belongs to Kalleh

from the database. During

but 42 customers belong to Kalleh Company, so we remov

Total number of customer after removing noisy data became 2457.

4.1.2 Data Transformation result:

4.1.2.1 Generalization:

The result of generalization that is explained in Chapter3, is 6 category of

products which is shown in F

Figure 4.1: generalized product category

4.1.2.2 RFM Construction:

behavior patterns we need, customer behavioral variables. This part of the

has been done in the SQL server 2000. F

dataset to two time snapshot, one between '1383/07/01' AND '1384/06/31' as period

one or t1 and the second one between '1384/07/01' AND '1385/06/31' as period two

or t2.

Product Categories

Category1: Dairy Products

Category2: Ice-Cream

Category3: Meat Products(freezed or non-freezed

Category5: Pitza Cheese

Category11: Sauces

Category13: Cooking Cheese & processed cheese

processing phase of analysis has been done in SQL server 2000

and the data-mining phase performs with R package

). This chapter shows the analysis and result of each step in change

Data preprocessing result: 4.1.1 Data Cleaning: According to chapter3, we have some noisy data

which are the customers who belongs to Kalleh Company. So we removed them

two periods that we analyze, there were 2499 customers

to Kalleh Company, so we remove them from the database.

Total number of customer after removing noisy data became 2457.

4.1.2 Data Transformation result:

4.1.2.1 Generalization:


products which is shown in Figure 4.1

generalized product category

4.1.2.2 RFM Construction: As explained in chapter2, for building the customer

behavior patterns we need, customer behavioral variables. This part of the

has been done in the SQL server 2000. For calculating RFM, first we divided our



Product Categories

: Dairy Products

Cream

: Meat Products(freezed or

: Pitza Cheese

: Sauces

: Cooking Cheese & processed

71

processing phase of analysis has been done in SQL server 2000

mining phase performs with R package (R

). This chapter shows the analysis and result of each step in change

According to chapter3, we have some noisy data

. So we removed them

two periods that we analyze, there were 2499 customers

e them from the database.


As explained in chapter2, for building the customer

behavior patterns we need, customer behavioral variables. This part of the research

or calculating RFM, first we divided our



We defined recency by calculating the interv

purchase and the last date of each period which for period. It means that the

evaluating time for these two time snapshots are '1384/06/31' and '1385/06/31'.

For frequency and monetary

total number of purchases and total amount spent during each period. According to

the market segmentation by

each customer. So we divide total purchase amount by total number of purchases to

calculate average amount of each purchase. The final data that is ready to the next

step for discretization has the format as

extracted from database can be seen.

Table 4.1: RFM table fields

4.2Customer segmentation (Marcus, C., 1998), we divided customers to four clusters in each period which

include uncertain, frequent, spender and best. According to Customer Value matrix,

we have two axes. The calculation steps of Cus

in the following section.

4.2.1 Customer Value Matrix Result:

Value Matrix in chapter3, we applied customer value matrix introduced by

C., 1998). For each period, we define

number of purchases and the other is average amount of purchase. In table 4.2, table

4.3, the results are shown.

Period 1:

RFM Table

•Period

•Customer Code(ID)

•Recency (days)

•Frequency

•Monetary (Average of purchase)

We defined recency by calculating the interval between the last date of

the last date of each period which for period. It means that the


monetary, we aggregate the transaction data to calc


the market segmentation by (Marcus, C., 1998), we need the average purchasing of


alculate average amount of each purchase. The final data that is ready to the next

step for discretization has the format as illustrated in table 4.1. The fields that are

extracted from database can be seen.

Table 4.1: RFM table fields

segmentation (in SQL server 2000): According to

), we divided customers to four clusters in each period which


we have two axes. The calculation steps of Customer Value Matrix and its result are

4.2.1 Customer Value Matrix Result: According to definition of Customer

Value Matrix in chapter3, we applied customer value matrix introduced by

). For each period, we define two variables for this matrix. One is average


4.3, the results are shown.

72

al between the last date of

the last date of each period which for period. It means that the


we aggregate the transaction data to calculate the


), we need the average purchasing of


alculate average amount of each purchase. The final data that is ready to the next

fields that are

According to

), we divided customers to four clusters in each period which


tomer Value Matrix and its result are

According to definition of Customer

Value Matrix in chapter3, we applied customer value matrix introduced by (Marcus,

two variables for this matrix. One is average


73

Table 4.2 : calculating variables for customer value matrix

Average number of purchases = total number of purchases/total number of customers

Total number of purchases = 16,424

Total number of customers = 789

Average number of purchases = 1,6424/789= 20.82 purchases per customer

Average purchase amount = total sales/total number of customers

Total sales average = 1,037,047,130.8 Rials


Average purchase amount 1,037,047,130.8 Rials /789= 1,314,381.66 rials per purchase

Period 2:

Table 4.3: calculating variables for customer value matrix

Average number of purchases = total number of purchases/total number of customers

Total number of purchases = 31,061


Average number of purchases = 31,061/2199= 14.1 purchases per customer

Average purchase amount = total sales/total number of customers

Total sales average = 2,829,589,665.4 Rials


Average purchase amount 2,829,589,665.4 Rials /2199= 1,286,762 per purchase

Based on the customer value matrix we have four clusters. For each customer,

we calculate average amount of purchase and the number of purchases. By

comparing these values with the two average variables calculated before, we

determine each customer belong to which cluster.

When average sale of each customer is less than the average of sales and

74

number of purchase is less than the average of frequency, called uncertain segment.

Else, when average sale of each customer is less than the average of sales and

number of purchase is greater than the average of frequency, called frequent

segment. When average sale of each customer is greater than the average of sales

and number of purchase is less than the average of frequency, called spender

segment. Finally, when average sale of each customer is greater than the average of

sales and number of purchase is greater than the average of frequency, called best

segment. Figure4.2 shows the four segments.

Figure 4.2: The Customer Value Matrix

The segmentation results in period one and two are shown in table 4.4, table 4.5

Table 4.4: segment information in For period 1.

Segment Number of

customers

Percentage

Uncertain 441 56%

Frequent 140 18%

Spender 85 11%

Frequency Avg.

Frequency

Monetary

Av

g.

Mo

net

ary

Spender

Best

Uncertain

Frequency

75

Best 123 16%

Total 789

Table 4.5: segment information in For period 2

Segment Number of

customers

Percentage

Uncertain 1335 61%

Frequent 389 18%

Spender 227 10%

Best 248 11%

Total 2199

Based on the Customer Value Matrix, as mentioned, for clusters are as follows: the

uncertain, frequent, spenders and best.

4.3Customer Behavior Mining: In this phase, we applied association

rules to analyze the patterns of customer behavior of different time snapshots for

each customer cluster. For mining changes in customer behavior during different

periods, we divide data to two periods and in each period, we build four clusters of

customers include uncertain, spender, frequent and best.

As mentioned, the purpose of this study is to mine customer behavior patterns

by building association rules while customer profile data and behavioral variables

(RFM) are in the conditional part and purchased products are as the consequent part.

The first issue is association rule mining work with discrete variables. Therefore, in

the first phase we need to do discretization.

4.3.1 Discretization Result: As explained in section in chapter3, we have various

types of discretization methods. In this study we did this step in R package. For

three customer behavioral variables, RFM, we have used equal frequency binning.

The result of this discretization is as follows. We build four quantiles by equal

frequency binning in R package for Recency, Frequency and Monetary. Here we

76

present the quantile data and histogram of R, F, M and Area separately.

Table4.6: R quantile Figure4.3: R histogram

77

Table 4.8: F quantile Figure 4.5: F histogram

Frequency

Variable

Quantile

interval

1th Quantile 1 to 2

2th Quantile 2 to 5

3th Quantile 5 to 20

4th Quantile 20 to

248

For discretizing the area, based on the market expert opinion and their knowledge

about the area we have define four groups.

78

4.3.2 Association Rule Mining Results:

We applied Apriori algorithm to mine association rules in this research. The

minimum support and confidence is 17% and we find maximal frequent itemsets.

After association rules built, the association rules of each cluster for two different

time periods are compared to understand the customer behavior patterns of the most

valuable customers. It means that we have 8 rulesets for four cluster o f customers

and in two periods.

4.4Change Mining: In this step, we compare each two rulesets related to each customer in two periods.

In table 4.6you can see the summary of generated rule in each cluster and in each

period.

Table 4.10: Generated rule summary

Number of generated association rules per cluster

Cluster Period1 Period2

1 20 13

79

2 76 65

3 22 29

4 127 86

All of the mined association rules and their change types are shown in table

4.6.

Here, in tables below, the rules that have been made by the Chen similarity

formula are seen. For each cluster, we have two ruleset for each period.

In each ruleset we can see different kinds of changes in customer purchasing

behavior. Cluster one is the cluster who buy frequently and their purchase amount is

below the average purchase amount of total. In the generated rules and the changes

in customer purchasing behavior, from the 5 kinds that we defined in the

methodology, we have found 4 kinds of them in four clusters. While, there are large

number of changes in customer behavior patterns, a few example of change pattern

are selected from each change type to provide an explanation.

4.4.1 Some examples of change pattern:

One example of emerging pattern in cluster1:

t1-r5: "Area=poor, -> cat1=1" support =0.191344

t2-r8: "Area=poor, -> cat1=1" support=0.260674 Cat1 is dairy products group

The growth rate is 36%.

This rule shows that the poor area generally buy dairy products. The support of this rule

show 36% growth means rule grows more robust over time.

One example of unexpected purchasing pattern in cluster1:

t1-r1: area=normal -> cat11=1 support =0.170843 cat11 is sauces

t2-r4: area=normal -> cat1=1 support =0.193258 cat1 is dairy products

The above rules show that the initial pattern of customer behavior is area=

normal to purchase sauces category. However, in the second period, this group

80

shows that they purchase dairy products. This unexpected consequent pattern can

lead marketing decision makers to enforce their marketing effort to know why this

change happened and promoting dairy products to this group and to reduce

promotion of sauces to normal area, thus increase customer value.

One example of Added Rule:

t1-r24: R=25% , -> cat2=1 support = 0.200514

While R = 25% means recency is between 0 to 5 days and cat2 is ice-cream

The above rule is a newly added pattern which provides a reference for

developing promotion plans to stimulate customer needs.

One example of Perished Rule:

t1-r11: F=100% ,M=50% -> cat1=1 support =0.178571 similarity=0.333

While F=100% means Frequency is between 20 to 248 times, M=50% means Monetary

is between 269471.154 Rials to 538398.005 Rials and Cat1 means Dairy products.

The above rule showed that during the first period, among customers whose

their frequency is between 24 and 248 times and their monetary expenditure is

between 269471.154 Rials to 538398.005 Rials bought dairy product but the

similarity of this rule with the generated rules in the next period is 0.33 which is

lower than rule matching threshold(RMT). In marketing when we face such a

situation, it means that the focus of marketing strategies should be changed from this

group. The unexpected purchasing (Consequent) and unexpected shifting

(condition) patterns can help to better determined where to focus.

4.4.2 Association rules and changes based (Chen et al, 2005): Table 4.11: Generated Rules for period 1 Cluster

1

Rule-

Index rule1 Support Change Type Similarity

Sim-

Rule-

Index

1 area=normal , ->

cat11=1 ,

0.170843 Unexpected perished 0 1

2 M=25% , -> cat1=1 , 0.177677 Emerging trend 1 7

3 M=50% , -> cat11=1 , 0.18451 Unexpected perished 0 1

4 area=poor , -> cat11=1 , 0.220957 Emerging trend 1 9

81



7 R=75% , -> cat11=1 , 0.170843 Unexpected perished 0 1

8 R=75% , -> cat1=1 , 0.193622 Emerging trend 1 2




12 M=75% , -> cat1=1 , 0.1959 Emerging trend 1 3



15 F=25% , -> cat11=1 , 0.189066 Emerging trend 1 13


17 F=75% , -> cat11=1 , 0.218679 Unexpected

purchasing

0 1


19 F=75% , -> cat5=1 , 0.230068 Unexpected

purchasing

0 1

20 F=75% , -> cat3=1 , 0.173121 Unexpected

purchasing

0 1

Table4.12: Generated Rules for period 2

Cluster 1

Rul-


Sim-

Rule-

Index

1 F=75% , -> cat1=1 , 0.18427 1 18

2 R=75% , -> cat1=1 , 0.170037 1 8

3 M=75% , -> cat1=1 , 0.175281 1 12

4 area=normal , ->

cat1=1 ,

0.193258 Unexpected

purchasing

0 1

5 R=100% , -> cat1=1 , 0.207491 Unexpected added 0 1

6 M=50% , -> cat1=1 , 0.229213 Unexpected added 0 1

7 M=25% , -> cat1=1 , 0.213483 1 2

8 area=poor , -> cat1=1 , 0.260674 1 5

9 area=poor , -> cat11=1

,

0.182772 1 4

10 area=poor , -> cat3=1 , 0.170787 1 6

82

11 F=25% , -> cat3=1 , 0.170787 Unexpected added 0 1

12 F=25% , -> cat1=1 , 0.277903 1 16

13 F=25% , -> cat11=1 , 0.183521 1 15

Generated rules for cluster2 are as follows:

Table4.13: Generated Rules for period 1 Cluster 2

Rul-

Index

rule1 Support Change Type Similarity Sim-

Rule-

Index

1 F=100% ,R=50% , -> cat5=1 , 0.178571 Not perished 0.5 12

2 F=100% ,area=good , -> cat1=1 , 0.178571 Perished 0.333333 25

3 F=100% ,area=good , -> cat5=1 , 0.178571 Not perished 0.5 18

4 area=good , -> cat11=1 , 0.178571 Unexpected

perished

0 1

5 F=100% ,area=rich , -> cat3=1 , 0.178571 Perished 0.333333 26

6 F=100% ,area=rich , -> cat11=1 , 0.178571 Perished 0.333333 27

7 F=100% ,area=rich , -> cat1=1 ,cat5=1 , 0.178571 Perished 0.333333 21

8 F=100% ,M=50% , -> cat2=1 , 0.171429 Perished 0.166667 21

9 F=100% ,M=50% , -> cat3=1 , 0.178571 Not perished 0.5 5

10 F=100% ,M=50% , -> cat5=1 ,cat11=1 , 0.178571 Perished 0.333333 22

11 F=100% ,M=50% , -> cat1=1 , 0.178571 Perished 0.333333 25

12 M=50% , -> cat1=1 ,cat5=1 , 0.171429 Not perished 0.5 4

13 F=100% ,area=normal , -> cat3=1 ,cat13=1 , 0.171429 Not perished 0.5 14



16 area=normal , -> cat1=1 ,cat13=1 , 0.171429 Not perished 0.5 13


18 F=100% ,area=normal , -> cat1=1 ,cat3=1 , 0.171429 Emerging trend 1 20


20 F=100% ,area=normal , -> cat1=1 ,cat5=1 ,cat11=1

,

0.171429 Not perished 0.666667 19

21 area=normal , -> cat2=1 , 0.171429 Unexpected

perished

0 1

22 F=100% ,R=25% ,M=75% , -> cat1=1 ,cat3=1 , 0.171429 Not perished 0.666667 30


83



26 F=100% ,M=75% , -> cat3=1 ,cat11=1 ,cat13=1 , 0.171429 Emerging trend 1 38



29 M=75% , -> cat5=1 ,cat11=1 ,cat13=1 , 0.171429 Not perished 0.75 34


31 F=100% ,M=75% , -> cat1=1 ,cat5=1 ,cat13=1 , 0.178571 Not perished 0.666667 33


33 M=75% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1 , 0.192857 Emerging trend 1 44




37 M=75% , -> cat2=1 , 0.171429 Unexpected

perished

0 1

38 F=100% ,R=25% , -> cat2=1 ,cat3=1 ,cat11=1 , 0.171429 Not perished 0.666667 53

39 R=25% , -> cat1=1 ,cat2=1 ,cat3=1 ,cat11=1 , 0.178571 Not perished 0.75 55








47 F=100% , -> cat2=1 ,cat3=1 ,cat11=1 ,cat13=1 , 0.178571 Not perished 0.75 23






53 F=100% , -> cat1=1 ,cat2=1 ,cat3=1 ,cat11=1 , 0.257143 Emerging trend 1 23



84


57 F=100% ,R=25% , -> cat3=1 ,cat11=1 ,cat13=1 , 0.221429 Emerging trend 1 53

58 R=25% , -> cat3=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.214286 Emerging trend 1 50




















Rule-


Sim-

Rule-

Index

1 area=rich , -> cat1=1 , 0.177378 Added 0.25 7

2 area=poor , -> cat1=1 ,cat3=1 , 0.187661 Unexpected added 0 1

3 area=poor , -> cat11=1 , 0.172237 Unexpected added 0 1

4 M=50% , -> cat1=1 ,cat11=1 , 0.172237 Not Added 0.5 12

5 M=50% , -> cat3=1 , 0.190231 Not Added 0.5 9

6 M=50% , -> cat5=1 , 0.179949 Not Added 0.5 12

85

7 F=75% , -> cat1=1 ,cat11=1 , 0.179949 Unexpected added 0 1

8 F=75% , -> cat1=1 ,cat3=1 , 0.197943 Unexpected added 0 1

9 R=50% , -> cat1=1 ,cat13=1 , 0.172237 Unexpected

purchasing

0 1


purchasing

0 1


purchasing

0 1

12 R=50% , -> cat5=1 , 0.192802 Not Added 0.5 1

13 area=normal , -> cat1=1 ,cat3=1 ,cat11=1 ,cat13=1

,

0.172237 Not Added 0.5 16

14 F=100% ,area=normal , -> cat13=1 , 0.174807 Not Added 0.5 13

15 area=normal , -> cat5=1 ,cat13=1 , 0.18509 Not Added 0.5 15

16 area=normal , -> cat1=1 ,cat5=1 ,cat11=1 , 0.177378 Not Added 0.5 20

17 area=normal , -> cat1=1 ,cat3=1 ,cat5=1 , 0.182519 Added 0.333333 16

18 F=100% ,area=normal , -> cat5=1 , 0.172237 Not Added 0.5 1

19 F=100% ,area=normal , -> cat1=1 ,cat11=1 , 0.190231 Not Added 0.666667 20

20 F=100% ,area=normal , -> cat1=1 ,cat3=1 , 0.179949 1 18

21 F=100% , -> cat1=1 ,cat2=1 ,cat5=1 , 0.179949 Not Added 0.75 52

22 F=100% , -> cat2=1 ,cat5=1 ,cat11=1 , 0.177378 Not Added 0.75 51

23 F=100% , -> cat1=1 ,cat2=1 ,cat3=1 ,cat11=1 , 0.174807 1 53

24 R=25% , -> cat2=1 , 0.200514 Added 0.25 39

25 F=100% ,R=25% ,M=75% , -> cat1=1 , 0.195373 Not Added 0.5 22

26 F=100% ,R=25% ,M=75% , -> cat3=1 , 0.18509 Not Added 0.5 22

27 F=100% ,R=25% ,M=75% , -> cat11=1 , 0.174807 Not Added 0.5 23

28 R=25% ,M=75% , -> cat1=1 ,cat11=1 , 0.190231 Not Added 0.666667 23

29 R=25% ,M=75% , -> cat3=1 ,cat11=1 , 0.182519 Added 0.333333 22

30 R=25% ,M=75% , -> cat1=1 ,cat3=1 , 0.195373 Not Added 0.666667 22

31 R=25% ,M=75% , -> cat5=1 , 0.192802 Added 0.333333 24

32 R=25% ,M=75% , -> cat13=1 , 0.182519 Added 0.166667 26

33 F=100% ,M=75% , -> cat3=1 ,cat5=1 ,cat13=1 , 0.179949 1 28

34 M=75% , -> cat1=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.190231 Not Added 0.75 29



37 F=100% ,M=75% , -> cat1=1 ,cat11=1 ,cat13=1 , 0.187661 Not Added 0.666667 26

38 F=100% ,M=75% , -> cat3=1 ,cat11=1 ,cat13=1 , 0.18509 1 26

86

39 F=100% ,M=75% , -> cat1=1 ,cat3=1 ,cat13=1 , 0.192802 1 27


41 F=100% ,M=75% , -> cat1=1 ,cat5=1 ,cat11=1 , 0.197943 1 36

42 F=100% ,M=75% , -> cat3=1 ,cat5=1 ,cat11=1 , 0.197943 1 34

43 F=100% ,M=75% , -> cat1=1 ,cat3=1 ,cat5=1 , 0.213368 1 35

44 M=75% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1 , 0.226221 1 33

45 F=100% ,M=75% , -> cat1=1 ,cat3=1 ,cat11=1 , 0.233933 1 32

46 F=100% ,R=25% , -> cat1=1 ,cat5=1 ,cat13=1 , 0.182519 1 66

47 F=100% ,R=25% , -> cat3=1 ,cat5=1 ,cat13=1 , 0.187661 1 62

48 F=100% ,R=25% , -> cat5=1 ,cat11=1 ,cat13=1 , 0.172237 1 65

49 R=25% , -> cat1=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.174807 1 64

50 R=25% , -> cat3=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.177378 1 58

51 R=25% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat13=1 , 0.187661 1 61

52 F=100% ,R=25% , -> cat1=1 ,cat11=1 ,cat13=1 , 0.192802 1 63

53 F=100% ,R=25% , -> cat3=1 ,cat11=1 ,cat13=1 , 0.195373 1 57

54 F=100% ,R=25% , -> cat1=1 ,cat3=1 ,cat13=1 , 0.203085 1 60

55 R=25% , -> cat1=1 ,cat3=1 ,cat11=1 ,cat13=1 , 0.200514 1 59

56 F=100% ,R=25% , -> cat1=1 ,cat5=1 ,cat11=1 , 0.197943 1 71

57 F=100% ,R=25% , -> cat3=1 ,cat5=1 ,cat11=1 , 0.205656 1 69

58 F=100% ,R=25% , -> cat1=1 ,cat3=1 ,cat5=1 , 0.215938 1 70

59 R=25% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1 , 0.208226 1 68

60 F=100% ,R=25% , -> cat1=1 ,cat3=1 ,cat11=1 , 0.244216 1 67

61 F=100% , -> cat1=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.257069 1 75

62 F=100% , -> cat3=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.25964 1 73

63 F=100% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat13=1 , 0.272494 1 74

64 F=100% , -> cat1=1 ,cat3=1 ,cat11=1 ,cat13=1 , 0.298201 1 72

65 F=100% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1 , 0.303342 1 76


Rul-


Sim-

Rule-

Index

1 F=50% ,M=100% , -> cat5=1 , 0.188235294 Emerging

trend

1 15

87

2 M=100% ,area=good , -> cat5=1 , 0.188235294 Not perished 0.5 1

3 R=50% ,M=100% , -> cat5=1 , 0.211764706 Emerging

trend

1 1

4 F=25% ,M=100% , -> cat5=1 , 0.211764706 Emerging

trend

1 9

5 R=75% ,M=100% , -> cat5=1 , 0.188235294 Emerging

trend

1 6

6 M=100% ,area=poor , -> cat11=1 , 0.176470588 Not perished 0.5 14

7 M=100% ,area=poor , -> cat5=1 , 0.258823529 Not perished 0.5 1

8 M=100% ,area=normal , -> cat5=1 , 0.235294118 Emerging

trend

1 4

9 R=100% ,M=100% , -> cat5=1 , 0.282352941 Emerging

trend

1 12

10 F=75% ,M=100% , -> cat1=1 ,cat13=1 , 0.2 Not perished 0.5 16



13 F=75% ,M=100% , -> cat5=1 ,cat13=1 , 0.2 Emerging

trend

1 16

14 M=100% , -> cat1=1 ,cat3=1 ,cat11=1 ,cat13=1 , 0.211764706 Emerging

trend

1 25


trend

1 27


trend

1 26


trend

1 28






trend

1 29


Rule-

Index rule1 Support

Change

Type Similarity

Sim-

Rule-

Index

1 R=50% ,M=100% , -> cat5=1 , 0.171806 1 3

88

2 M=100% ,area=normal , -> cat1=1 , 0.185022 Added 0.25 10

3 M=100% ,area=normal , -> cat3=1 , 0.180617 Added 0.25 11

4 M=100% ,area=normal , -> cat5=1 , 0.189427 1 8

5 R=75% ,M=100% , -> cat3=1 , 0.171806 Added 0.25 11

6 R=75% ,M=100% , -> cat5=1 , 0.193833 1 5

7 F=25% ,M=100% , -> cat1=1 , 0.171806 Added 0.25 10

8 F=25% ,M=100% , -> cat3=1 , 0.171806 Added 0.25 11

9 F=25% ,M=100% , -> cat5=1 , 0.220264 1 4

10 R=100% ,M=100% , -> cat1=1 ,cat11=1 , 0.171806 Added 0.333333 18

11 R=100% ,M=100% , -> cat3=1 ,cat11=1 , 0.171806 Added 0.333333 18

12 R=100% ,M=100% , -> cat5=1 , 0.229075 1 9

13 F=50% ,M=100% , -> cat1=1 ,cat3=1 , 0.171806 Added 0.333333 18

14 F=50% ,M=100% , -> cat11=1 , 0.1982

38

Not

Added

0.5 6

15 F=50% ,M=100% , -> cat5=1 , 0.22467 1 1

16 F=75% ,M=100% , -> cat5=1 ,cat13=1 , 0.180617 1 13

17 F=75% ,M=100% , -> cat1=1 , 0.189427 Not

Added

0.5 10

18 F=75% ,M=100% , -> cat3=1 ,cat11=1 , 0.193833 Not

Added

0.666667 18

19 F=75% ,M=100% , -> cat5=1 ,cat11=1 , 0.180617 Not

Added

0.666667 20

20 F=75% ,M=100% , -> cat3=1 ,cat5=1 , 0.202643 Not

Added

0.666667 19

21 M=100% ,area=poor , -> cat1=1 , 0.198238 Added 0.25 10

22 M=100% ,area=poor , -> cat3=1 ,cat11=1 , 0.185022 Not

Added

0.5 6

89


Added

0.5 6


Added

0.5 7

25 M=100% , -> cat1=1 ,cat3=1 ,cat11=1

,cat13=1 ,

0.23348 1 14

26 M=100% , -> cat1=1 ,cat5=1 ,cat11=1

,cat13=1 ,

0.237885 1 16

27 M=100% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat13=1

,

0.264317 1 15

28 M=100% , -> cat3=1 ,cat5=1 ,cat11=1

,cat13=1 ,

0.264317 1 17

29 M=100% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1

,

0.303965 1 22


Rule


Sim-

Rule-

Index

1 F=100% ,M=100% ,area=rich , -> cat3=1 , 0.178862 Emerging

trend

1 4

2 F=100% ,M=100% ,area=rich , -> cat5=1 , 0.195122 Not perished 0.666667 30

3 M=100% , -> cat2=1 ,cat11=1 , 0.170732 Not perished 0.666667 11


5 F=100% ,M=100% , -> cat1=1 ,cat2=1 ,cat5=1 , 0.178862 Emerging

trend

1 16

6 R=25% ,M=100% ,area=poor , -> cat3=1 ,cat5=1 , 0.170732 Not perished 0.666667 10

7 F=100% ,R=25% ,area=poor , -> cat3=1 ,cat5=1 , 0.170732 Not perished 0.666667 65

8 F=100% ,R=25% ,M=100% ,area=poor , -> cat3=1 , 0.170732 Not perished 0.5 4

9 R=25% ,M=100% ,area=poor , -> cat5=1 ,cat13=1 , 0.186992 Not perished 0.666667 57

10 F=100% ,R=25% ,area=poor , -> cat5=1 ,cat13=1 , 0.186992 Not perished 0.666667 57



90

13 M=100% ,area=poor , -> cat1=1 ,cat3=1 ,cat11=1 , 0.170732 Not perished 0.666667 8

14 area=poor , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1 , 0.170732 Perished 0.25 8



17 F=100% ,area=poor , -> cat3=1 ,cat11=1 ,cat13=1 , 0.170732 Not perished 0.5 34




21 F=100% ,M=100% ,area=poor , -> cat3=1 ,cat11=1 , 0.178862 Not perished 0.666667 45














91










44 M=100% ,area=normal , -> cat3=1 ,cat5=1 ,cat11=1 , 0.170732 Not perished 0.666667 25


46 area=normal , -> cat1=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.170732 Perished 0.25 24


48 F=100% ,area=normal , -> cat1=1 ,cat5=1 ,cat11=1 , 0.170732 Not perished 0.5 43

49 F=100% ,M=100% ,area=normal , -> cat1=1 ,cat11=1 , 0.170732 Not perished 0.666667 24






55 area=normal , -> cat1=1 ,cat3=1 ,cat5=1 ,cat13=1 , 0.170732 Perished 0.375 28

56 M=100% ,area=normal , -> cat1=1 ,cat3=1 ,cat5=1 , 0.178862 Emerging

trend

1 31

57 M=100% ,area=normal , -> cat3=1 ,cat5=1 ,cat13=1 , 0.178862 Emerging

trend

1 28









66 F=100% ,R=25% ,M=100% ,area=good , -> cat1=1 , 0.186992 Not perished 0.5 3

67 F=100% ,R=25% ,M=100% ,area=good , -> cat13=1 , 0.186992 Not perished 0.75 19

68 F=100% ,M=100% ,area=good , -> cat1=1 ,cat11=1 , 0.178862 Not perished 0.666667 42


92

70 F=100% ,M=100% ,area=good , -> cat3=1 , 0.186992 Not perished 0.666667 4



73 R=25% ,M=100% , -> cat1=1 ,cat3=1 ,cat11=1 , 0.439024 Emerging

trend

1 48

74 F=100% ,R=25% , -> cat1=1 ,cat3=1 ,cat11=1 , 0.422764 Emerging

trend

1 44

75 R=25% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1 , 0.406504 Emerging

trend

1 49


trend

1 38


trend

1 39


trend

1 34


trend

1 40


trend

1 51


trend

1 46

82 F=100% ,R=25% ,M=100% , -> cat3=1 ,cat11=1 , 0.479675 Emerging

trend

1 45


trend

1 36


trend

1 35


trend

1 37


trend

1 50


trend

1 43


trend

1 42


trend

1 41


trend

1 33


trend

1 32


trend

1 47


trend

1 58

93


trend

1 54


trend

1 59


trend

1 66


trend

1 63


trend

1 62


trend

1 61


trend

1 56


trend

1 55


trend

1 65


trend

1 60


trend

1 53


trend

1 52


trend

1 64


trend

1 57


trend

1 73

109 F=100% , -> cat1=1 ,cat3=1 ,cat11=1 ,cat13=1 , 0.528455 Emerging

trend

1 69


trend

1 80


trend

1 77


trend

1 76


trend

1 75


trend

1 71


trend

1 70

116 F=100% ,M=100% , -> cat3=1 ,cat5=1 ,cat11=1 , 0.601626 Emerging 1 79

94

trend


trend

1 74


trend

1 68


trend

1 67


trend

1 78


trend

1 72


trend

1 85


trend

1 82


trend

1 81


trend

1 86


trend

1 84


trend

1 83


Rule-

Index rule1 Support Change

Type Similarity

Sim-

Rule-

Index

1 R=50% ,M=100% , -> cat3=1 , 0.173387 Added 0.333333 1

2 M=100% ,area=rich , -> cat13=1 , 0.177419 Added 0.25 11

3 F=100% ,M=100% ,area=rich , -> cat1=1 , 0.173387 Not Added 0.5 66

4 F=100% ,M=100% ,area=rich , -> cat3=1 , 0.177419 1 1

5 M=100% ,area=rich , -> cat1=1 ,cat3=1 , 0.181452 Added 0.333333 1

6 M=100% ,area=rich , -> cat3=1 ,cat5=1 , 0.173387 Added 0.333333 1

7 M=100% ,area=poor , -> cat13=1 , 0.173387 Not Added 0.5 11

8 M=100% ,area=poor , -> cat1=1 ,cat3=1 , 0.177419 Not Added 0.666667 13


95


11 M=100% , -> cat1=1 ,cat2=1 ,cat11=1 , 0.181452 Not Added 0.666667 3



14 M=100% , -> cat2=1 ,cat13=1 , 0.173387 Not Added 0.5 3

15 F=100% ,M=100% , -> cat1=1 ,cat2=1 ,cat3=1 , 0.173387 Not Added 0.666667 5

16 F=100% ,M=100% , -> cat1=1 ,cat2=1 ,cat5=1 , 0.173387 1 5


18 M=100% ,area=good , -> cat11=1 , 0.181452 Added 0.333333 68

19 F=100% ,M=100% ,area=good , -> cat13=1 , 0.189516 Not Added 0.75 67

20 M=100% ,area=good , -> cat5=1 ,cat13=1 , 0.173387 Added 0.333333 9

21 F=100% ,M=100% ,area=good , -> cat3=1 ,cat5=1 , 0.177419 Not Added 0.666667 38

22 M=100% ,area=good , -> cat1=1 ,cat3=1 , 0.173387 Added 0.333333 13

23 M=100% ,area=good , -> cat1=1 ,cat5=1 , 0.173387 Not Added 0.666667 72

24 M=100% ,area=normal , -> cat1=1 ,cat11=1 , 0.181452 Not Added 0.666667 45




28 M=100% ,area=normal , -> cat3=1 ,cat5=1 ,cat13=1 , 0.185484 1 57

29 F=100% ,M=100% ,area=normal , -> cat3=1 , 0.173387 Not Added 0.666667 1

30 F=100% ,M=100% ,area=normal , -> cat5=1 , 0.185484 Not Added 0.666667 2

31 M=100% ,area=normal , -> cat1=1 ,cat3=1 ,cat5=1 , 0.177419 1 56

32 F=100% ,R=25% ,M=100% , -> cat11=1 ,cat13=1 , 0.375 1 91

33 F=100% ,R=25% , -> cat5=1 ,cat11=1 ,cat13=1 , 0.358871 1 90

34 F=100% ,R=25% , -> cat3=1 ,cat11=1 ,cat13=1 , 0.350806 1 78

35 F=100% ,R=25% , -> cat1=1 ,cat11=1 ,cat13=1 , 0.326613 1 84

36 R=25% ,M=100% , -> cat1=1 ,cat11=1 ,cat13=1 , 0.346774 1 83

37 R=25% , -> cat1=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.330645 1 85

38 R=25% , -> cat1=1 ,cat3=1 ,cat11=1 ,cat13=1 , 0.326613 1 76

39 R=25% ,M=100% , -> cat3=1 ,cat11=1 ,cat13=1 , 0.375 1 77

40 R=25% , -> cat3=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.362903 1 79

41 R=25% ,M=100% , -> cat5=1 ,cat11=1 ,cat13=1 , 0.383065 1 89

42 F=100% ,R=25% ,M=100% , -> cat1=1 ,cat11=1 , 0.370968 1 88

96

43 F=100% ,R=25% , -> cat1=1 ,cat5=1 ,cat11=1 , 0.350806 1 87

44 F=100% ,R=25% , -> cat1=1 ,cat3=1 ,cat11=1 , 0.350806 1 74

45 F=100% ,R=25% ,M=100% , -> cat3=1 ,cat11=1 , 0.395161 1 82

46 F=100% ,R=25% , -> cat3=1 ,cat5=1 ,cat11=1 , 0.379032 1 81

47 F=100% ,R=25% ,M=100% , -> cat5=1 ,cat11=1 , 0.403226 1 92

48 R=25% ,M=100% , -> cat1=1 ,cat3=1 ,cat11=1 , 0.383065 1 73

49 R=25% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1 , 0.366935 1 75

50 R=25% ,M=100% , -> cat1=1 ,cat5=1 ,cat11=1 , 0.387097 1 86

51 R=25% ,M=100% , -> cat3=1 ,cat5=1 ,cat11=1 , 0.415323 1 80

52 F=100% ,R=25% ,M=100% , -> cat1=1 ,cat13=1 , 0.399194 1 105

53 F=100% ,R=25% , -> cat1=1 ,cat5=1 ,cat13=1 , 0.375 1 104

54 F=100% ,R=25% , -> cat1=1 ,cat3=1 ,cat13=1 , 0.370968 1 94

55 F=100% ,R=25% ,M=100% , -> cat3=1 ,cat13=1 , 0.439516 1 101

56 F=100% ,R=25% , -> cat3=1 ,cat5=1 ,cat13=1 , 0.419355 1 100

57 F=100% ,R=25% ,M=100% , -> cat5=1 ,cat13=1 , 0.455645 1 107

58 R=25% ,M=100% , -> cat1=1 ,cat3=1 ,cat13=1 , 0.395161 1 93

59 R=25% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat13=1 , 0.379032 1 95

60 R=25% ,M=100% , -> cat1=1 ,cat5=1 ,cat13=1 , 0.399194 1 103

61 R=25% ,M=100% , -> cat3=1 ,cat5=1 ,cat13=1 , 0.451613 1 99

62 F=100% ,R=25% ,M=100% , -> cat1=1 ,cat3=1 , 0.423387 1 98

63 F=100% ,R=25% , -> cat1=1 ,cat3=1 ,cat5=1 , 0.403226 1 97

64 F=100% ,R=25% ,M=100% , -> cat1=1 ,cat5=1 , 0.431452 1 106

65 F=100% ,R=25% ,M=100% , -> cat3=1 ,cat5=1 , 0.471774 1 102

66 R=25% ,M=100% , -> cat1=1 ,cat3=1 ,cat5=1 , 0.443548 1 96

67 F=100% ,M=100% , -> cat1=1 ,cat11=1 ,cat13=1 , 0.447581 1 119

68 F=100% , -> cat1=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.419355 1 118

69 F=100% , -> cat1=1 ,cat3=1 ,cat11=1 ,cat13=1 , 0.427419 1 109

70 F=100% ,M=100% , -> cat3=1 ,cat11=1 ,cat13=1 , 0.479839 1 115

71 F=100% , -> cat3=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.455645 1 114

72 F=100% ,M=100% , -> cat5=1 ,cat11=1 ,cat13=1 , 0.475806 1 121

73 M=100% , -> cat1=1 ,cat3=1 ,cat11=1 ,cat13=1 , 0.491935 1 108

74 M=100% , -> cat1=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.487903 1 117

75 M=100% , -> cat3=1 ,cat5=1 ,cat11=1 ,cat13=1 , 0.524194 1 113

97

76 F=100% ,M=100% , -> cat1=1 ,cat3=1 ,cat11=1 , 0.491935 1 112

77 F=100% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1 , 0.455645 1 111

78 F=100% ,M=100% , -> cat1=1 ,cat5=1 ,cat11=1 , 0.471774 1 120

79 F=100% ,M=100% , -> cat3=1 ,cat5=1 ,cat11=1 , 0.508065 1 116

80 M=100% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat11=1 , 0.560484 1 110

81 F=100% ,M=100% , -> cat1=1 ,cat3=1 ,cat13=1 , 0.512097 1 124

82 F=100% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat13=1 , 0.479839 1 123

83 F=100% ,M=100% , -> cat1=1 ,cat5=1 ,cat13=1 , 0.508065 1 127

84 F=100% ,M=100% , -> cat3=1 ,cat5=1 ,cat13=1 , 0.560484 1 126

85 M=100% , -> cat1=1 ,cat3=1 ,cat5=1 ,cat13=1 , 0.552419 1 122

86 F=100% ,M=100% , -> cat1=1 ,cat3=1 ,cat5=1 , 0.544355 1 125

4.4.3 Rules with discrete variables in RHS: According to the (Chen et al,

2005), in the RHS parts of the rules, we have the customer buy the products or not.

In this section, we build some rules that show how many times a product category

bought and compare them by Chen similarity formula.

Then we have modified Chen similarity formula by Manhattan distance

formula which calculates difference between the values of each attribute in two

rules.

For each cluster we have 4 ruleset. For each period we have one itemset and

we compare generated rules once by Chen similarity formula and the other time by

modified formula with Manhattan distance. We have two steps, first discretizing the

number of purchases for each product category and the second is generating

association rules and comparing them.

1. Discretization of number f purchases for each product category:

First, before starting, we discretize the frequency of purchases of each product

for each customer during time. It means that if cat1=20 then the selected customer

purchase cat1, 20 times in the selected period. The number of purchases for each

product category discretized and its results with their histogram are as followed.

98

99

100

Table4.21: Cat3 quantile Figure4.9: Cat3 histogram

Category3 Quantile

Variable interval

1th Quantile 0 to 0

2th Quantile 0 to 1

3th Quantile 1 to 9


101

102

Category11 Quantile

Variable interval

1th Quantile 0 to 0

2th Quantile 0 to 1

3th Quantile 1 to 5



103

Category13 Quantile

Variable interval

1th Quantile 0 to 0

2th Quantile 0 to 0

3th Quantile 1 to 2


4.4.4 Change mining with Manhattan distance

The outcome of generated rules and changes are as followed.

Cluster1: Change mining by (Chen et al, 2005) measures & by Manhattan distance

Table4.25: Generated Rules for period 1 Cluster 1, Change mining by (Chen et al, 2005) measures & Manhattan distance

Rule

Index rule1 Support Change

Type

Similarity Change

Type -M

Similarity

-M

Sim-

Rule -

Index1

Sim-

Rule-

Index2

1 F=0.25 , ->

cat11=0.25 ,

0.12984 Emerging

trend

1.000 Emerging

trend

1.000 2 2

2 F=0.25 , ->

cat3=0.25 ,

0.10251 Emerging

trend

1.000 Emerging

trend

1.000 1 1

3 F=0.25 , ->

cat1=0.25 ,

0.14123 Not perished 0.500 Not

perished

0.500 4 4

4 R=1 , ->

cat1=0.25 ,


perished

0.500 4 4


104

5 R=0.75 , ->

cat1=0.25 ,

0.11162 Unexpected

perished 0.000 Perished 0.375 1 4

6 M=0.25 , ->

cat1=0.25 ,


perished

0.500 5 5

7 F=0.25 , ->

cat5=0.25 ,

0.13212 Emerging

trend

1.000 Emerging

trend

1.000 3 3

8 M=0.75 , ->

cat5=0.25 ,

0.12301 Unexpected

perished

0.000 Unexpected

perished

0.000 1 1

9 R=1 , ->

cat5=0.25 ,

0.10023 Unexpected

perished

0.000 Unexpected

perished

0.000 1 1

10 R=0.75 , ->

cat5=0.25 ,

0.10251 Unexpected

perished

0.000 Unexpected

perished

0.000 1 1

Table4.26: Generated Rules Table 4.26: Generated rules for period 2 Cluster 1, Change mining by (Chen et al, 2005) measures &

Rule

Index

rule 2 Support

Change

Type

Similarity Change

Type -

M

Similarity

-M

Sim-

Rule

Inde

x1

Sim-

Rule

Index2

1 F=0.25 , -

>

cat3=0.25 ,

0.12734 1.000 1.000 2 2

2 F=0.25 , -

>

cat11=0.25

,

0.13483 1.000 1.000 1 1

3 F=0.25 , -

>

cat5=0.25 ,

0.12509 1.000 1.000 7 7

4 F=0.25

,R=1 , ->

cat1=0.25 ,

0.10037 Not

Added

0.500 Not

Added

0.500 3 3

5 F=0.25

,M=0.25 ,

->

cat1=0.25

0.10337 Not

Added

0.500 Not

Added

0.500 3 3

105

6 F=0.25

,area=1 , -

>

cat1=0.25

0.10337 Not

Added

0.500 Not

Added

0.500 3 3

Cluster2:Change mining by (Chen et al, 2005) measures & by Manhattan distance

Table4.27: Generated Rules for period 1 Cluster 2, Change mining by(Chen et al, 2005) measures &

Manhattan distance

Rule


Change

Type -M

Similarity

-M

Sim-

Rule-

Index

1

Sim-

Rule-

Index

2

1 F=1 , -> cat1=0.25 , 0.10714 Unexpected

purchasing

0.000 Not

perished

0.500 1 11

2 F=1 , -> cat11=0.25 , 0.10714 Unexpected

purchasing

0.000 Not

perished

0.500 1 1

3 F=1 , -> cat11=0.75 , 0.12857 Emerging

trend

1.000 Emerging

trend

1.000 1 1

4 F=1 ,R=0.25 , ->

cat5=0.75 ,


perished

0.500 6 6

5 F=1 , -> cat3=0.75

,cat11=1 ,


perished

0.583 8 27

6 F=1 ,R=0.25 , ->

cat3=0.75 ,


perished

0.500 8 8

7 M=0.75 , ->

cat3=0.75 ,

0.10000 Emerging

trend

1.000 Emerging

trend

1.000 9 9

8 F=1 ,M=0.75 , ->

cat1=0.75 ,


perished

0.656 11 22

9 F=1 , -> cat1=0.75

,cat5=1 ,


perished

0.500 11 11

106

10 F=1 ,R=0.25 , ->

cat1=0.75 ,


perished

0.656 11 24

11 area=0.5 , ->

cat1=0.75 ,

0.10000 Unexpected

perished

0.000 Not

perished

0.563 1 7

12 F=1 ,R=0.5 , ->

cat5=1 ,


perished

0.500 17 17

13 F=1 ,area=0.75 , ->

cat13=0.75 ,


perished

0.500 19 19

14 F=1 ,M=0.75 , ->

cat13=0.75 ,


perished

0.500 19 19

15 F=1 ,R=0.25 , ->

cat13=0.75 ,


perished

0.750 19 4

16 F=1 ,area=0.5 , ->

cat5=1 ,


perished

0.500 17 17

17 F=1 , -> cat1=1

,cat13=1 ,

0.13571 Perished 0.333 Perished 0.375 27 11

18 F=1 , -> cat5=1

,cat13=1 ,


perished

0.500 16 16

19 F=1 ,R=0.25 , ->

cat13=1 ,

0.14286 Emerging

trend

1.000 Emerging

trend

1.000 4 4

20 M=0.75 , -> cat13=1

,

0.10000 Emerging

trend

1.000 Emerging

trend

1.000 5 5

21 F=1 ,R=0.25

,area=0.25 , ->

cat1=1 ,


perished

0.833 35 35

22 F=1 ,area=0.25 , ->

cat5=1 ,


perished

0.500 17 17

23 F=1 ,M=0.5 , ->

cat2=1 ,


perished

0.500 2 2

24 F=1 ,M=0.5 , ->

cat1=1 ,

0.10714 Emerging

trend

1.000 Emerging

trend

1.000 22 22

25 F=1 ,M=0.5 , ->

cat5=1 ,


perished

0.875 17 17

26 R=0.25 , -> cat2=1

,cat3=1 ,


perished

0.500 3 3

27 F=1 ,area=0.75 , ->

cat3=1 ,

0.10714 Emerging

trend

1.000 Emerging

trend

1.000 28 28

28 F=1 ,R=0.25 , ->

cat3=1 ,cat11=1 ,


perished

0.500 26 26

29 F=1 ,R=0.25 , ->

cat1=1 ,cat3=1 ,

0.10000 Emerging

trend

1.000 Emerging

trend

1.000 30 30

107

Table4. 28: Generated Rules for period 2 Cluster 2, Change mining by (Chen e t al, 2005) measures

&Manhattan distance

Rule

Index

rule2 Support Change

Type Similarity

Change

Type -M

Similarity

-M

Sim-

Rule-

Index1

Sim-

Rule-

Index2

1 F=1 , ->

cat11=0.75 ,

0.10283 1.000 1.000 3 3

2 F=1 , -> cat2=1

,

0.12339 Not Added 0.500 Not

Added

0.500 23 23

3 R=0.25 , ->

cat2=1 ,


Added

0.500 26 26

4 F=1 ,R=0.25 , -

> cat13=1

0.10026 1.000 1.000 19 19

5 M=0.75 , ->

cat13=1 ,

0.10283 1.000 1.000 20 20

6 F=1 , ->

cat5=0.75 ,


Added

0.500 4 4

7 area=0.25 , ->

cat 1=1 ,

0.10540 Added 0.333 Not

Added

0.563 21 11

8 F=1 , ->

cat3=0.75 ,


Added

0.500 5 5

9 M=0.75 , ->

cat3=0.75 ,

0.11825 1.000 1.000 7 7

10 F=0.75 , ->

cat3=0.75 , 0.10283

Unexpected

added 0.000 Added 0.375 1

5

F=1 , ->

cat1=0.75 ,


Added

0.500 8 1

12 R=0.25 , ->

cat1=0.75 ,


Added

0.500 10 10

13 M=0.75 , ->

cat1=0.75


Added

0.500 8 8

108

14 F=0.75 , ->

cat1=0.75 , 0.10283

Unexpected


1

15 area=1 , ->

cat1=1 , 0.13882

Unexpected


11

16

F=1 , -> cat5=1

,cat11=1 , 0.11054 Not Added 0.500 Not

Added 0.500 5

5

17 F=1 ,M=0.75 , -

> cat5=1 , 0.14139 Not Added 0.667 Not

Added 0.875 44

25

18

F=1 ,R=0.25 , -

> cat1=1

,cat5=1 ,

0.10026

1.000

1.000 43

43

19 F=1 , ->

cat13=0.75 ,


Added

0.500 13 13

20 R=0.25 , ->

cat13=0.75 ,


Added

0.500 15 15

21 M=0.75 , ->

cat13=0.75 ,


Added

0.750 14 20

F=1 ,M=0.5 , ->

cat1=1 ,

0.11311 1.000 1.000 24 24

23 F=0.75 , ->

cat1=1 , 0.10026

Unexpected

added 0.000 Added 0.375 1 17

24 F=1 ,R=0.5 , ->

cat1=1 ,


Added

0.656 24 10

25 R=0.5 , ->

cat11=1 , 0.10540

Unexpected

added 0.000 Added 0.250 1 40

26

F=1 ,M=0.75 , -

> cat3=1 ,cat

=1 ,


Added 0.500 28 28

27

F=1 , -> cat1=1

,cat3=1 ,cat

=1 ,

0.10540 Added 0.333 Not

Added 0.583 5 5

28 F=1 ,area=0.75 ,

-> cat3=1 , 0.10540

1.000

1.000 27 27

29

F=1 ,R=0.25

,M=0.75 , >

cat3=1 ,


Added 0.667 30 30

30

F=1 ,R=0.25 , -

> cat1=1

,cat3=1 ,

0.11311

1.000

1.000 29 29

31 F=1 ,area=0.75 ,

-> cat =1 , 0.10797

1.000

1.000 36 36

109

32

F=1 ,M=0.75 , -

> cat1=1 ,cat

=1 ,


Added 0.500 39 39

F=1 ,R=0.25

,M=0.75 , >

cat11=1 ,

0.10026

1.000

1.000 40 40

34

F=1 ,R=0.25 , -

> cat1=1 ,cat

=1 ,

0.10283

1.000

1.000 39 39

35

F=1 ,R=0.25

,area=0.75 , ->

cat 1=1 ,

0.10540

1.000

1.000 37 37

36

F=1 ,R=0.25

,M=0.75 , >

cat1=1 ,

0.12339

1.000

1.000 42 42


Table 4.29: Generated Rules for period 1 Cluster 3, Change mining by (Chen et al, 2005) measures &

Manhattan distance

Rule

Index rule1 Support

Change

Type Similarity

Change

Type -M

Similarity

-M

Sim-

Rule-

Index1

Sim-

Rule-

Index2

1 M=1 , ->

cat11=0.25 ,

0.10588 Emerging

trend

1.000 Emerging

trend

1.000 14 14

2 M=1 , ->

cat1=0.75 ,

0.10588 Emerging

trend

1.000 Emerging

trend

1.000 10 10

3 M=1 , ->

cat3=0.25 ,

0.11765 Emerging

trend

1.000 Emerging

trend

1.000 1 1

4 M=1 , ->

cat3=0.5 ,

0.12941 Emerging

trend

1.000 Emerging

trend

1.000 4 4

5 M=1 , ->

cat11=0.5 ,

0.14118 Emerging

trend

1.000 Emerging

trend

1.000 5 5

6 M=1 , ->

cat13=0.5 ,

0.15294 Emerging

trend

1.000 Emerging

trend

1.000 8 8

7 F=0.75 ,M=1 , ->

cat3=0.75 ,

0.12941 Emerging

trend

1.000 Emerging

trend

1.000 19 19

110

8 F=0.5 ,M=1 , ->

cat5=0.5 ,

0.12941 Not

perished

0.500 Not

perished

0.656 7 11

9 F=0.75 ,M=1

,area=0.5 , ->

cat5=0.75 ,

0.11765 Not

perished

0.667 Not

perished

0.667 11 11

10 F=0.75 ,M=1 , ->

cat =0.75,

0.11765 Not

perished

0.500 Not

perished

0.500 6 6

11 R=1 ,M=1 , ->

cat5=0.5 ,

0.10588 Not

perished

0.500 Not

perished

0.750 7 21

12 R=0.5 ,M=1 , ->

cat5=0.75 ,

0.10588 Not

perished

0.500 Not

perished

0.500 11 11

13 M=1 , ->

cat13=0.25 ,

0.22353 Emerging

trend

1.000 Emerging

trend

1.000 9 9

14 F=0.25 ,R=1

,M=1 , ->

cat5=0.25 ,

0.12941 Not

perished

0.667 Not

perished

0.667 15 15

15 F=0.75 ,M=1 , ->

cat1=0.25 ,

0.15294 Not

perished

0.500 Not

perished

0.750 13 13

16 F=0.75 ,R=0.75

,M =1 , ->

cat5=0.75 ,

0.10588 Not

perished

0.667 Not

perished

0.667 11 11

17 M=1 ,area=1 , ->

cat5=0.75 , 0.10588 Emerging

trend

1.000 Emerging

trend

1.000 12 12

Table4. 30: Generated Rules for period 2 Cluster 3, Change mining by(Chen et al, 2005) measures &

Manhattan distance

Rule

Inde

x rule1 Support

Change

Type Similarity

Change

Type -M

Similarity

-M

Sim-

Rule-

Index1

Sim-Rule-

Inde x2

1 M=1 , -> cat3=0.25

,

0.12775 1.000 1.000 3 3

111

2 M=1 , ->

cat13=0.75 ,

0.12775 Unexpected

purchasing

0.000 Not Added 0.750 1 6

3 M=1 , -> cat 1=0.5

,

0.14097 Unexpected

purchasing

0.000 Not Added 0.750 1 2

4 M=1 , -> cat 3=0.5

,

0.14537 1.000 1.000 4 4

5 M=1 , -> cat11=0.5

,

0.14978 1.000 1.000 5 5

6 M=1 , ->

cat11=0.75 ,

0.14097 Not Added 0.500 Not Added 0.750 10 5

7 M=1 , -> cat 5=0.5

,


8 M=1 , -> cat13=0.5

,

0.17181 1.000 1.000 6 6

9 M=1 , ->

cat13=0.25 ,

0.18062 1.000 1.000 13 13

10 M=1 , -> cat1=0.75

,

0.18943 1.000 1.000 2 2

11 F=0.75 ,M=1 , ->

cat5=0.75 ,


12 M=1 ,area=1 , ->

cat5=0.75 ,

0.11454 1.000 1.000 17 17

13 F=0.25 ,M=1 , ->

cat1=0.25 ,


14 M=1 , ->

cat11=0.25 ,

0.22467 1.000 1.000 1 1

15 F=0.25 ,M=1

,area=1 , ->

cat5=0.25 ,


16 R=1 ,M=1 , ->

cat3=0.75 ,


17 M=1 , -> cat

3=0.75 ,cat5=0.25 ,

0.11013 Added 0.250 Added 0.375 7 4

18 F=0.5 ,M=1 , ->

cat3=0.75 ,


19 F=0.75 ,M=1 , ->

cat3=0.75 ,

0.10132 1.000 1.000 7 7

20 M=1 ,area=1 , ->

cat3=0.75 ,


21 R=1 ,M=1 , ->

cat5=0.25 , 0.13656 Not Added 0.667 Not Added 0.750 14 11

112


Table4.31: Generated Rules for period 1 Cluster 4, Change mining by (Chen et al, 2005) measures & Manhattan

distance

Rule

index rule1 Support

Change

Type Similarity

Change

Type -M

Similarity

-M

Sim-

Rule-

Index

1

Sim-

Rule

-

Index

2

1 M=1 , -> cat =0.5 , 0.10569 Emerging

trend

1.000 Emerging

trend

1.000 1 1

2 F=1 ,M=1 , -> cat2=1 , 0.10569 Unexpected

purchasing 0.000 Unexpected

purchasing 0.000 1 1

3 F=1 ,M=1 , -> cat3=0.75

,

0.13008 Not

perished

0.500 Not perished 0.500 4 4

4 F=1 ,R=0.25 ,M=1 , ->

cat1=0.75

0.12195 Not

perished

0.667 Not perished 0.667 12 12

5 F=1 ,M=1 , -> cat5=0.75

,

0.14634 Perished 0.250 Perished 0.375 6 4

6 F=1 ,M=1 , ->

cat1=0.25,cat5=1

0.12195 Not

perished

0. 500 Not perished 0.750 4 11

7 R=0.25 ,M =1 , ->

cat1=0.25 ,

0.10569 Not

perished

0.500 Not perished 0.500 2 2

8 F=1 ,R=0.25 ,M=1

,area=0.25 , -> cat5=1 , 0.10569

Emerging

trend 1.000

Emerging

trend 1.000 18 18

9 F=1 ,M=1 , -> cat11=1

,cat 13=0.75 ,

0.10569 Not

perished

0.500 Not perished 0.583 8 42

10 F=1 ,M=1 , -> cat5=1

,cat13=0.75 ,

0.14634 Emerging

trend

1.000 Emerging

trend

1.000 8 8

F=1 ,R=0.25 ,M=1 , ->

cat13=0.75 ,

0.11382 Not

perished

0.667 Not perished 0.667 9 9

12 M=1 ,area=1 , -> cat 3=1

,cat5=1 ,cat13=1 , 0.10569

Not

perished 0.667 Not perished 0.667 22

13 F=1 ,area=1 , -> cat3=1

,cat5=1 ,cat13=1 , 0.10569

Not

perished 0.500 Not perished 0.500 79 79

14

R=0.25,area=1 , ->

cat3=1 ,cat5=1 ,cat13=1

,

0.10569 Not


113

15 R=0.25 ,M =1 ,area=1 , -

> cat3=1 ,cat13=1 , 0.10569

Not


16 F=1 ,R=0.25 ,area=1 , ->

cat3=1 ,cat13=1 , 0.10569

Not


17 F=1 ,M=1 ,area =1 , ->

cat3=1 ,cat13=1 , 0.10569

Not


18 R=0.25,M =1 ,area=1 , -

> cat5=1 ,cat13=1 , 0.12195

Not


19 F=1 ,R=0.25 ,area=1 , ->

cat5=1 ,cat13=1 , 0.12195

Not


20 F=1 ,M=1 ,area =1 , ->

cat5=1 ,cat13=1 , 0.12195

Not


21 F=1 ,R=0.25 ,M=1

,area=1 , -> cat13=1 , 0.12195

Not


R=0.25 ,M =1 ,area=1 , -

> cat5=1 ,cat11=1 , 0.10569

Not


23 F=1 ,R=0.25 ,area=1 , ->

cat5=1 ,cat11=1 , 0.10569

Not


24 F=1 ,M=1 ,area =1 , ->

cat5=1 ,cat11=1 , 0.10569

Not


25 F=1 ,R=0.25 ,M=1

,area=1 , -> cat11=1 ,

0.12195 Not

perished

0.500 Not perished 0.563 16 16

26 R=0.25 ,M =1 ,area=1 , -

> cat3=1 ,cat5=1 , 0.13008

Not


27 F=1 ,R=0.25 ,area=1 , ->

cat3=1 ,cat5=1 , 0.13008

Not


28 F=1 ,M=1 ,area =1 , ->

cat3=1 ,cat5=1 , 0.13821

Not


29 F=1 ,R=0.25 ,M=1

,area=1 , -> cat3=1 , 0.13821

Not


30 F=1 ,R=0.25 ,M=1

,area=1 , -> cat5=1 , 0.17886

Emerging

trend 1.000

Emerging

trend 1.000 24 24

31 F=1 ,M=1 ,area =0.75 , -

> cat13=1 ,

0.11382 Emerging

trend

1.000 Emerging

trend

1.000 31 31

32 F=1 ,M=1 ,area =0.75 , -

> cat11=1 ,

0.13008 Not

perished

0.667 Not perished 0.833 16 16

F=1 ,M=1 ,area =0.75 , -

> cat3=1 ,

0.10569 Emerging

trend

1.000 Emerging

trend

1.000 32 32

34 R=0.25,M =1 ,area=0.75

, -> cat5=1 ,

0.10569 Not

perished

0.750 Not perished 0.750 33

35 F=1 ,M=1 ,area =0.75 , -

> cat5=1 ,

0.16260 Not

perished

0.750 Not perished 0.750 33

36 F=1 ,R=0.25 ,M=1

,area=0.5 , -> cat1=1 , 0.12195

Not


37 F=1 ,R=0.25 ,M=1

,area=0.5 , -> cat13=1 , 0.15447

Emerging

trend 1.000

Emerging

trend 1.000 26 26

114

38 F=1 ,M=1 ,area =0.5 , ->

cat3=1 ,cat11=1 , 0.10569

Not


39 F=1 ,R=0.25 ,M=1

,area=0.5 , -> cat11=1 , 0.12195

Not


40 F=1 ,R=0.25 ,M=1

,area=0.5 , -> cat3=1 , 0.13008

Emerging

trend 1.000

Emerging

trend 1.000 28 28

41 F=1 ,R=0.25 ,M=1

,area=0.5 , -> cat5=1 , 0.13821

Emerging

trend 1.000

Emerging

trend 1.000 29 29

42 M=1 , -> cat1=1 ,cat 3=1

,cat11=1 ,cat13=1 , 0.13821

Emerging

trend 1.000

Emerging

trend 1.000 34 34

43 F=1 , -> cat1=1 ,cat3=1

,cat11=1

0.13821 Emerging

trend

1.000 Emerging

trend

1.000 35 35

R=0.25 , -> cat1=1

,cat3=1

,cat11=1,cat13=1 ,

0.13008 Emerging

trend

1.000 Emerging

trend

1.000 36 36

45 M=1 , -> cat1=1 ,cat 5=1

,cat11=1 ,cat13=1 , 0.13821

Emerging

trend 1.000

Emerging

trend 1.000 37 37

46 F=1 , -> cat1=1 ,cat5=1

,cat11=1 ,cat13=1 , 0.13821

Emerging

trend 1.000

Emerging

trend 1.000 38 38

47

R=0.25 , -> cat1=1

,cat5=1 ,cat11=1

,cat13=1 ,

0.13821 Emerging

trend 1.000

Emerging

trend 1.000 39 39

48 R=0.25,M =1 , -> cat1=1

,cat11=1 ,cat13=1 , 0.16260

Emerging

trend 1.000

Emerging

trend 1.000 40 40

49 F=1 ,R=0.25 , -> cat 1=1

,cat11=1 ,cat13=1 , 0.16260

Emerging

trend 1.000

Emerging

trend 1.000 41 41

50 F=1 ,M=1 , -> cat1=1

,cat11=1 ,cat13=1 , 0.17073

Emerging

trend 1.000

Emerging

trend 1.000 42 42

51 M=1 , -> cat1=1 ,cat3=1

,cat5=1 ,cat13=1 , 0.13821

Emerging

trend 1.000

Emerging

trend 1.000 43 43

52 F=1 , -> cat1=1 ,cat3=1

,cat5=1 ,cat13=1 , 0.13821

Emerging

trend 1.000

Emerging

trend 1.000 44

53

R=0.25 , -> cat1=1

,cat3=1 ,cat5=1 ,cat13=1

,

0.13008 Emerging

trend 1.000

Emerging

trend 1.000 45 45

54 R=0.25,M =1 , -> cat1=1

,cat3=1 ,cat13=1 , 0.15447

Emerging

trend 1.000

Emerging

trend 1.000 46 46

F=1 ,R=0.25 , -> cat 1=1

,cat3=1 ,cat13=1 , 0.15447

Emerging

trend 1.000

Emerging

trend 1.000 47 47

56 F=1 ,M=1 , -> cat1=1

,cat3=1 ,cat13=1 , 0.17073

Emerging

trend 1.000

Emerging

trend 1.000 48 48

57 R=0.25,M =1 , -> cat1=1

,cat5=1 ,cat13=1 , 0.16260

Emerging

trend 1.000

Emerging

trend 1.000 49 49

58 F=1 ,R=0.25 , -> cat 1=1

,cat5=1 ,cat13=1 , 0.16260

Emerging

trend 1.000

Emerging

trend 1.000 50 50

115

59 F=1 ,M=1 , -> cat1=1

,cat5=1 ,cat13=1 , 0.17886

Emerging

trend 1.000

Emerging

trend 1.000 51 51

60 F=1 ,R=0.25 ,M=1 , ->

cat1=1 ,cat13=1 , 0.21138

Emerging

trend 1.000

Emerging

trend 1.000 52 52

61 M=1 , -> cat1=1 ,cat3=1

,cat5=1,cat11=1 ,

0.13821 Emerging

trend

1.000 Emerging

trend

1.000 53 53

62 F=1 , -> cat1=1 ,cat3=1

,cat5=1 ,cat11=1 , 0.13821

Emerging

trend 1.000

Emerging

trend 1.000 54 54

63

R=0.25 , -> cat1=1

,cat3=1 ,cat5=1 ,cat11=1

,

0.13821 Emerging

trend 1.000

Emerging

trend 1.000 55

64

R=0.25 ,M =1 , ->

cat1=1 ,cat3=1 ,cat11=1

,

0.16260 Emerging

trend 1.000

Emerging

trend 1.000 56 56

65 F=1 ,R=0.25 , -> cat 1=1

,cat3=1 ,cat11=1 , 0.16260

Emerging

trend 1.000

Emerging

trend 1.000 57 57

F=1 ,M=1 , -> cat1=1

,cat3=1 ,cat11=1 , 0.17886

Emerging

trend 1.000

Emerging

trend 1.000 58 58

67 R=0.25,M =1 , -> cat1=1

,cat5=1 ,cat11=1 , 0.18699

Emerging

trend 1.000

Emerging

trend 1.000 59 59

68 F=1 ,R=0.25 , -> cat 1=1

,cat5=1 ,cat11=1 , 0.18699

Emerging

trend 1.000

Emerging

trend 1.000 60 60

69 F=1 ,M=1 , -> cat1=1

,cat5=1 ,cat11=1 , 0.19512

Emerging

trend 1.000

Emerging

trend 1.000 61 61

70 F=1 ,R=0.25 ,M=1 , ->

cat1=1 ,cat11=1 , 0.21951

Emerging

trend 1.000

Emerging

trend 1.000 62 62

71 R=0.25,M =1 , -> cat1=1

,cat3=1 ,cat5=1 , 0.16260

Emerging

trend 1.000

Emerging

trend 1.000 63 63

72 F=1 ,R=0.25 , -> cat 1=1

,cat3=1 ,cat5=1 , 0.16260

Emerging

trend 1.000

Emerging

trend 1.000 64 64

73 F=1 ,M=1 , -> cat1=1

,cat3=1 ,cat5=1 , 0.17886

Emerging

trend 1.000

Emerging

trend 1.000 65 65

74 F=1 ,R=0.25 ,M=1 , ->

cat1=1 ,cat3=1 , 0.19512

Emerging

trend 1.000

Emerging

trend 1.000 66

75 F=1 ,R=0.25 ,M=1 , ->

cat1=1 ,cat5=1 , 0.21951

Emerging

trend 1.000

Emerging

trend 1.000 67 67

76 M=1 , -> cat3=1 ,cat 5=1

,cat11=1 ,cat13=1 , 0.15447

Emerging

trend 1.000

Emerging

trend 1.000 68 68

F=1 , -> cat3=1 ,cat5=1

,cat11=1 ,cat13=1 , 0.15447

Emerging

trend 1.000

Emerging

trend 1.000 69 69

78 R=0.25 , -> cat3=1

,cat5=1

,cat11=1,cat13=1 ,

0.14634 Emerging

trend

1.000 Emerging

trend

1.000 70 70

79

R=0.25 ,M =1 , ->

cat3=1 ,cat11=1

,cat13=1 ,

0.17073 Emerging

trend 1.000

Emerging

trend 1.000 71 71

116

80 F=1 ,R=0.25 , -> cat 3=1

,cat11=1 ,cat13=1 , 0.17073

Emerging

trend 1.000

Emerging

trend 1.000 72 72

81 F=1 ,M =1 , -> cat3=1

,cat11=1 ,cat13=1 , 0.18699

Emerging

trend 1.000

Emerging

trend 1.000 73 73

82 R=0.25,M =1 , -> cat5=1

,cat11=1 ,cat13=1 , 0.17886

Emerging

trend 1.000

Emerging

trend 1.000 74 74

83 F=1 ,R=0.25 , -> cat 5=1

,cat11=1 ,cat13=1 , 0.17886

Emerging

trend 1.000

Emerging

trend 1.000 75 75

84 F=1 ,M=1 , -> cat5=1

,cat11=1 ,cat13=1 , 0.18699

Emerging

trend 1.000

Emerging

trend 1.000 76 76

85 F=1 ,R=0.25 ,M=1 , ->

cat11=1 ,cat13=1 , 0.22764

Emerging

trend 1.000

Emerging

trend 1.000 77

86 R=0.25,M =1 , -> cat3=1

,cat5=1 ,cat13=1 , 0.23577

Emerging

trend 1.000

Emerging

trend 1.000 78 78

87 F=1 ,R=0.25 , -> cat 3=1

,cat5=1 ,cat13=1 , 0.23577

Emerging

trend 1.000

Emerging

trend 1.000 79 79

F=1 ,M=1 , -> cat3=1

,cat5=1 ,cat13=1 , 0.25203

Emerging

trend 1.000

Emerging

trend 1.000 80 80

89 F=1 ,R=0.25 ,M=1 , ->

cat3=1 ,cat13=1 , 0.28455

Emerging

trend 1.000

Emerging

trend 1.000 81 81

90 F=1 ,R=0.25 ,M=1 , ->

cat5=1 ,cat13=1 , 0.31707

Emerging

trend 1.000

Emerging

trend 1.000 82 82

91 R=0.25,M =1 , -> cat3=1

,cat5=1 ,cat11=1 , 0.19512

Emerging

trend 1.000

Emerging

trend 1.000 83 83

92 F=1 ,R=0.25 , -> cat 3=1

,cat5=1 ,cat11=1 , 0.19512

Emerging

trend 1.000

Emerging

trend 1.000 84 84

93 F=1 ,M=1 , -> cat3=1

,cat5=1 ,cat11=1 , 0.22764

Emerging

trend 1.000

Emerging

trend 1.000 85 85

94 F=1 ,R=0.25 ,M=1 , ->

cat3=1 ,cat11=1 , 0.25203

Emerging

trend 1.000

Emerging

trend 1.000 86 86

95 F=1 ,R=0.25 ,M=1 , ->

cat5=1,cat11=1 ,

0.30081 Emerging

trend

1.000 Emerging

trend

1.000 87 87

96 F=1 ,R=0.25 ,M=1 , ->

cat3=1 ,cat5=1 , 0.30081

Emerging

trend 1.000

Emerging

trend 1.000 88

117

Table4.32: Generated Rules for period 2 Cluster 4, Change mining by (Chen et al, 2005) measures & Manhattan

distance

Rule

Index rule1 Support

Change

Type Similarity

Change

Type -

M Similarity

-M

Sim-

Rule-

Index1

Sim-

Rule-

Index2

1 M=1 , -> cat11=0.5 , 0.10081 1.000 1.000 1 1

2 3 M=1 , -> cat1=0.25 , 0.11290 Not Added 0.500 Not

Added

0.500 7 7

3 M=1 , -> cat 11=0.75

,

0.12097 Unexpected

purchasing

0.000 Not

Added

0.750 1 1

4 F=1 ,M =1 , ->

cat3=0.75 ,cat5=1 ,


Added

0.583 3 73

5 R=0.25 ,M=1 , ->

cat3=0.75 ,


Added

0.500 3 3

6 M=1 , -> cat3=1

,cat5=0.75 ,

0.10484 Added 0.250 Not

Added

0.438 5 51

7 M=1 , -> cat3=1 ,cat

13=0.75 ,

0.11290 Added 0.250 Not

Added

0.438 9 42

8 F=1 ,M=1 , -> cat5=1

,cat13=0.75 ,

0.11290 1.000 1.000 10 10

9 R=0.25 ,M=1 , ->

cat13=0.75 ,


Added

0.667 11

10 M=1 , -> cat1=0.75

,cat3=1 ,

0. 11290 Added 0.250 Not

Added

0.438 42 42

F=1 ,M =1 , ->

cat1=0.75 ,cat5=1 ,


Added

0.750 6 6

12 R=0.25 ,M=1 , ->

cat1=0.75 ,


Added

0.667 4 4

13 F=1 ,M =1 ,area=0.25

, -> cat1=1 ,


Added

0.688 36 36

14 F=1 ,M=1 ,area=0.25

, -> cat13=1 ,


Added

0.833 31 31

15 M=1 ,area=0.25 , ->

cat3=1 ,cat11=1 ,

0.10484 Added 0.333 Not

Added

0.583 38 38

16 F=1 ,M=1 ,area=0.25

, > cat =1 ,


Added

0.833 32 32

17 F=1 ,M =1 ,area=0.25

, -> cat3=1 ,


Added

0.833 33

18 F=1 ,R=0.25 ,M=1

,area=0.25 , -> cat5=1

,

0.10081 1.000 1.000 8 8

118

19 M=1 ,area=1 , -> cat

1=1 ,

0.10081 Added 0.250 Added 0.375 36 36

20 M=1 ,area=1 , -> cat

13=1 ,


Added

0.583 21 31

21 M=1 ,area=1 , -> cat

11=1 ,


Added

0.583 25 32

M=1 ,area=1 , ->

cat3=1 ,cat5=1 ,


Added

0.667 12 12

23 F=1 ,M =1 ,area=1 , -

> cat3=1 ,


Added

0.917 29

24 F=1 ,R=0.25 ,M=1

,area=1 , -> cat5=1 ,

0.10081 1.000 1.000 30 30

25 M=1 ,area=0.5 , ->

cat1=1 ,


Added

0.500 36 36

26 F=1 ,R=0.25 ,M=1

,area=0.5 , -> cat13=1

,

0.10887 1.000 1.000 37 37

27 M=1 ,area=0.5 , ->

cat11=1 ,


Added

0.583 39 32

28 F=1 ,R=0.25 ,M=1

,area=0.5 , -> cat3=1 ,

0.10887 1.000 1.000 40 40

29 F=1 ,R=0.25 ,M=1

,area=0.5 , -> cat5=1 ,

0.12097 1.000 1.000 41 41

30 M=1 ,area=0.75 , ->

cat1=1 ,

0.10081 Added 0.250 Not

Added

0.438 36 36

31 F=1 ,M=1 ,area=0.75

, -> cat13=1 ,

0.10081 1.000 1.000 31 31

32 F=1 ,M =1 ,area=0.75

, -> cat3=1 ,

0.11694 1.000 1.000 33

F=1 ,R=0.25 ,M=1

,area=0.75 , -> cat5=1

,


Added

0.938 8 30

34 M=1 , -> cat 1=1

,cat3=1 ,cat =1

,cat13=1 ,

0.12903 1.000 1.000 42 42

35 F=1 , -> cat1=1

,cat3=1 ,cat11=1

,cat13=1 ,

0.12500 1.000 1.000 43 43

36 R=0.25 , -> cat1=1

,cat3=1 ,cat11=1

,cat13=1 ,

0.10484 1.000 1.000 44

37 M=1 , -> cat 1=1

,cat5=1 ,cat =1

,cat13=1 ,

0.13710 1.000 1.000 45 45

38 F=1 , -> cat1=1

,cat5=1 ,cat11=1

0.13306 1.000 1.000 46 46

119

,cat13=1 ,

39 R=0.25 , -> cat1=1

,cat5=1 ,cat11=1

,cat13=1 ,

0.11694 1.000 1.000 47 47

40 R=0.25,M =1 , ->

cat1=1 ,cat11=1

,cat13=1 ,

0.13306 1.000 1.000 48 48

41 F=1 ,R=0.25 , ->

cat1=1 ,cat =1

,cat13=1 ,

0.13306 1.000 1.000 49 49

42 F=1 ,M =1 , -> cat1=1

,cat11=1 ,cat13=1 ,

0.16532 1.000 1.000 50 50

43

M=1 , -> cat1=1

,cat3=1 ,cat5=1

,cat13=1 ,

0.12097

1.000

1.000 51 51

F=1 , -> cat1=1

,cat3=1 ,cat5=1

,cat13=1 ,

0.11694 1.000 1.000 52 52

45 R=0.25 , -> cat1=1

,cat3=1 ,cat5=1

,cat13=1 ,

0.10887 1.000 1.000 53 53

46 R=0.25 ,M=1 , ->

cat1=1 ,cat3=1

,cat13=1 ,

0.13306 1.000 1.000 54 54

47 F=1 ,R=0.25 , ->

cat1=1 ,cat3=1

,cat13=1 ,

0.13306 1.000 1.000 55

48 F=1 ,M =1 , -> cat1=1

,cat3=1 ,cat13=1 ,

0.15323 1.000 1.000 56 56

49 R=0.25 ,M=1 , ->

cat1=1 ,cat5=1

,cat13=1 ,

0.14919 1.000 1.000 57 57

50 F=1 ,R=0.25 , ->

cat1=1 ,cat5=1

,cat13=1 ,

0.14919 1.000 1.000 58 58

51 F=1 ,M =1 , -> cat1=1

,cat5=1 ,cat13=1 ,

0.16532 1.000 1.000 59 59

52 F=1 ,R=0.25 ,M=1 , -

> cat1=1 ,cat13=1 ,

0.18548 1.000 1.000 60 60

53 M=1 , -> cat1=1

,cat3=1 ,cat5=1 ,cat

=1 ,

0.13306 1.000 1.000 61 61

54 F=1 , -> cat1=1


=1 ,

0.12903 1.000 1.000 62 62

120

R=0.25 , -> cat1=1


=1 ,

0.10484 1.000 1.000 63 63

56 R=0.25 ,M=1 , ->

cat1=1 ,cat3=1 ,cat

=1 ,

0.13306 1.000 1.000 64 64

57 F=1 ,R=0.25 , ->

cat1=1 ,cat3=1 ,cat

=1 ,

0.12903 1.000 1.000 65 65

58 F=1 ,M =1 , -> cat1=1

,cat3=1 ,cat =1 ,

0.18145 1.000 1.000 66

59 R=0.25 ,M=1 , ->

cat1=1 ,cat5=1 ,cat

=1 ,

0.13710 1.000 1.000 67 67

60

F=1 ,R=0.25 , ->

cat1=1 ,cat5=1 ,cat

=1 ,

0.13710

1.000

1.000 68 68

61 F=1 ,M =1 , -> cat1=1

,cat5=1 ,cat =1 ,

0.16935 1.000 1.000 69 69

62 F=1 ,R=0.25 ,M=1 , -

> cat1=1 ,cat =1 ,

0.16129 1.000 1.000 70 70

63 R=0.25 ,M=1 , ->

cat1=1 ,cat3=1

,cat5=1 ,

0.14113 1.000 1.000 71 71

64 F=1 ,R=0.25 , ->

cat1=1 ,cat3=1

,cat5=1 ,

0.14113 1.000 1.000 72 72

65 F=1 ,M=1 , > cat1=1

,cat3=1 ,cat5=1 ,

0.16935 1.000 1.000 73 73

F=1 ,R=0.25 ,M=1 , -

> cat1=1 ,cat3=1 ,

0.18145 1.000 1.000 74 74

67 F=1 ,R=0.25 ,M=1 , -

> cat1=1 ,cat5=1 ,

0.19758 1.000 1.000 75 75

68 M=1 , -> cat 3=1

,cat5=1 ,cat =1

,cat13=1 ,

0.15323 1.000 1.000 76 76

69 F=1 , -> cat3=1

,cat5=1 ,cat11=1

,cat13=1 ,

0.14113 1.000 1.000 77

70 R=0.25 , -> cat3=1

,cat5=1 ,cat11=1

,cat13=1 ,

0.12903 1.000 1.000 78 78

71 R=0.25 ,M=1 , ->

cat3=1 ,cat11=1

,cat13=1 ,

0.15323 1.000 1.000 79 79

121

72 F=1 ,R=0.25 , ->

cat3=1 ,cat =1

,cat13=1 ,

0.14919 1.000 1.000 80 80

73 F=1 ,M =1 , -> cat3=1

,cat11=1 ,cat13=1 ,

0.18548 1.000 1.000 81 81

74 R=0.25 ,M=1 , ->

cat5=1 ,cat11=1

,cat13=1 ,

0.16935 1.000 1.000 82 82

75 F=1 ,R=0.25 , ->

cat5=1 ,cat =1

,cat13=1 ,

0.16532 1.000 1.000 83 83

76 F=1 ,M =1 , -> cat5=1

,cat11=1 ,cat13=1 ,

0.18952 1.000 1.000 84 84

F=1 ,R=0.25 ,M=1 , -

> cat11=1 ,cat13=1 , 0.18952

1.000

1.000 85 85

78 R=0.25 ,M=1 , ->

cat3=1 ,cat5=1

,cat13=1 ,

0.19355 1.000 1.000 86 86

79 F=1 ,R=0.25 , ->

cat3=1 ,cat5=1

,cat13=1 ,

0.18952 1.000 1.000 87 87

80 F=1 ,M =1 , -> cat3=1

,cat5=1 ,cat13=1 ,

0.21371 1.000 1.000 88

81 F=1 ,R=0.25 ,M=1 , -

> cat3=1 ,cat13=1 ,

0.22984 1.000 1.000 89 89

82 F=1 ,R=0.25 ,M=1 , -

> cat5=1 ,cat13=1 ,

0.26613 1.000 1.000 90 90

83 R=0.25 ,M=1 , ->

cat3=1 ,cat5=1 ,cat

=1 ,

0.16935 1.000 1.000 91 91

84 F=1 ,R=0.25 , ->

cat3=1 ,cat5=1 ,cat

=1 ,

0.16532 1.000 1.000 92 92

85 F=1 ,M =1 , -> cat3=1

,cat5=1 ,cat =1 ,

0.20161 1.000 1.000 93 93

86 F=1 ,R=0.25 ,M=1 , -

> cat3=1 ,cat =1 ,

0.19758 1.000 1.000 94 94

87 F=1 ,R=0.25 ,M=1 , -

> cat5=1 ,cat =1 ,

0.22984 1.000 1.000 95 95

88 F=1 ,R=0.25 ,M=1 , -

> cat3=1,cat5=1 ,

0.29032 1.000 1.000 96 96

122

Here, for better explanation, we compare two rules similarity by measures of

(Chen et al, 2005) and by our modified measure with Manhattan distance.

For example in cluster 1, we have two rule s as followed:

T2-r5: R=0.75, -> cat1=0.25

(Chen et al, 2005)'s similarity= 0.000

Our similarity= 0.375 with the t1-r4: F=0.25, R=1, -> cat1=0.25.

This means that in the first method, because R has different values in two time

snapshots, the similarity become zero but at least these rules both has R in their LHS

but their values are different. We calculated the difference based on the distance

between the two R values in two rules and gain more information.

Another example in cluster4:

T2-r3:M=1 -> cat11=0.75

(Chen et al, 2005)'s similarity= 0.000

Our similarity= 0.750 with the t1-r1: M=1 -> cat11=0.5.

Again, by the (Chen et al, 2005)'s similarity, the similarity become zero, while,

Cat11 is in the both RHSs. We calculated the difference based on the distance

between the two R values in two rules and gain more information, because the

similarities of these rules are not zero. In this chapter we explain about the steps that

we have done to mine changes in customer behavior. Our contribution in this study

is using Manhattan distance to gain more information from the rules and increase

the accuracy of the change measures. In average, we have 6.65% improvement in

the change mining measures by using Manhattan distance.

123

Chapter5: Conclusion, further research

Conclusion

Our contribution

Limitation

Managerial implication

Future works

124

5.1Conclusion: In this study, we mined the purchasing behavior of Kalleh

Distribution Company. The world around us changes constantly. One of the most

important aspects of surviving in a dynamic market is to know and adapt to changes

happening in customer behavior. Knowing and adapting to changes is an important

aspect of our lives. For businesses, knowing what is changing and how it has

changed is also crucial (Liu et al, 2000). In Fast Moving Consumer Goods (FMCG)

Distribution Company like Kalleh, this issue has importance. Kalleh is faced with

the challenge of increasing competition. There are variety of FMCGs products,

distribution companies and their different strategies so in such a market; the

customer behavior may change by the trend of companies’ strategies in the market

and also by changing their need by themselves.

In order to combat with these problems, Kalleh Company wants to find

changes happening in the market by analyzing purchase transaction data. For mining

changes, we should compare customer purchasing behavior during two periods. The

purpose of this study is to mine changes in customer purchasing behavior. In order

to reach this goal we need to building customer purchasing patterns of customers

based on the customer, product and transaction data collected in databases.

Data mining techniques can help us to reach this goal. Change mining has

some steps including data collection, data pre-processing, customer segmentation

based on RFM and by Customer Value Matrix, building customer behavior patterns

by mining association rule and finally comparing generated association rule by two

measures of similarity and unexpectedness. In this study research process is shown

in figure 3.3. This process constructed based on the previous works in the literature.

In data collection phase, we gathered data from two years of transactions of Kalleh

Distribution Company. In the data pre-processing phase we have some steps as

followed, Data Cleaning is one of data preprocessing steps to remove noisy or

inconsistent data. In this study, we have some noisy data which are the customers

who belongs to Kalleh Company. So we removed them from the database. During

two periods that we analyze, there were 2499 customers but 42 customers belong to

Kalleh Company, so we remove them from the database. Total number of customer

after removing noisy data became 2457.

In Data Transformation phase in one step we did generalization that we build 6

groups of products by expert opinion which is shown in fig 4.1. The second task in

data transformation, we build RFM variables. For calculating RFM, first we divided

our dataset to two time snapshot, one between '1383/07/01' AND '1384/06/31' as

125

period one or t1 and the second one between '1384/07/01' AND '1385/06/31' as

period two or t2. We defined recency by calculating the interval between the last

date of purchase and the last date of each period which for period. It means that the

evaluating time for these two time snapshots are '1384/06/31' and '1385/06/31'. For

frequency and monetary, we aggregate the transaction data to calculate the total

number of purchases and total amount spent during each period. According to the

market segmentation by (Marcus, C., 1998), we need the average purchasing of each

customer. So we divide total purchase amount by total number of purchases to

calculate average amount of each purchase.

Customer segmentation is the next step in this study. According to (Marcus,

C., 1998), we divided customers to four clusters in each period which include

uncertain, frequent, spender and best. According to Customer Value matrix, we have

two axes. The calculation steps of Customer Value Matrix and its result are in the

following section. In table 4.4, tables 4.5, the results of market segmentation are

shown. We built four clusters of customers include uncertain, spender, frequent and

best.

The next step is customer behavior mining. In this phase, we applied

association rules to analyze the patterns of customer behavior of different time

snapshots for each customer cluster. There are some other methods for change

mining in the literature like decision trees but we have chosen association rules

because according to (Song et al, 2001) by them we can detect complete sets of

changes. Association rule work s with discrete variables, therefore, in the first phase

we need to do discretization. We have used the equal frequency binning to discretize

the RFM data. For discretizing the area, based on the market expert opinion and

their knowledge about the area we have define four groups. Also we have

discretized the number of purchases for each product category. In this phase we built

association rule which in the left hand side of the rule customer profile data and

RFM variables exists and in the right hand side of the rule, purchased product. The

minimum support and confidence is 17%. Also in Apriori algorithm we have used

maximum frequent itemsets. After building association rules, we compared these

rule to mine changes in customer behavior. We have two measures: similarity and

unexpectedness that evaluate how much two rules are similar or different from

(Chen et al, 2005). We calculated changes for each cluster in two periods that are

shown in chapter4.

126

5.2Our contribution: The next step was to building customer behavior patterns which in their RHS,

instead of saying just which products were bought, the number of purchase per

product category mentioned. When we use ordinal number instead of binary values

of bought or not bought, we bring more information. When we compare the values

of common attribute in LHS and RHS of two rules, we have more accuracy to find

difference and similarities between rules. This time the support and confidence of

rules were 10%. Then mining changes in generated rule is done by (Chen et al,

2005) measures and by Manhattan distance formula in chapter3, section 3.12. The

results showed that 6.5% in average the accuracy of the change mining improves

which is our contribution in this study.

5.3Limitation: In doing this research we have some limitation. One of them

is finding a good database that saves useful attributes in it. In our study we need

demographic variables of the customer but in the database we just found the

geographic area of each customer to work.

5.4Managerial Implication: In this section, we summarize the various

opportunities of using change mining methodology. The findings of this study have

great implication for many businesses like distribution companies. These companies

should work in a dynamic environment. Their customers are influenced by different

internal and external factors. In such a dynamic environment, knowing the changes

and adapting to them for businesses are crucial. In macro aspects, business managers

can follow the trend of the market in order to provide suitable products and services

for their customer (Liu et al, 2000). If marketing managers find the changes in the

market, they can find the reason of changes in time and show a right reaction to

changes. In Micro aspect, change mining can help managers to better understand

their customer needs by their behavior and design additional niche marketing

campaigns (Song et al, 2001). Change detection is more suitable in dynamic domain

where the human intervention is high. Another application of change mining can be

analyzing the effectiveness of marketing campaigns. Change mining can be used in

manufacturing to monitor changes and control the quality factors. Changes of vario

us measures of product quality can be properly controlled (Song et al, 2001).

Change mining can play an important role especially in FMCG market which

the competition is high. Also, because of huge amount of data that are recorded in

127

these companies database, using data mining methods like change mining bring

hidden and useful information from data. We believe that the change detection

problem will become more important as more data mining applications are

implemented.

5.5Future works: In this study, building rules we have just RFM variable and geographic

variable. It is because of the Kalleh database just stored these variables. There fore

the further research may be use other demographic variables like the type of the

customers.

In this research, for change mining we compare each rule in one time snapshot

s with all of the rules in the other time snapshot. Therefore the further research is to

do this comparison more efficiently.

References: Adomavicius, G. Tuzhilin, A., (2001), Using data mining methods to build customer

profiles. IEEE computer,Volume: 34, Issue: 2, pp.74-82

Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining assosiaction rules between

sets of items in large databases. In Proceedings of the ACM SIGMOD Conference

on management of data, (pp. 207-216)

Agrawal, R. & Psaila, G., (1995), Active Data Mining, First International

Conference on Knowledge Discovery and Data Mining ({KDD}-95), PP. 3-8.

Agrawal, R., & Srikant, R. (1994), Fast algorithem for mining association rules. In

Proceedings of the international Conference on Very Large Databases ,VLDB-94,

(pp.487-499)

Ayad, A. M., (2000), Incremental mining of constrained association rules, Master

Thesis, Alexandira University, Faculty of Engineering.

Bay, D.S. & Pazzani, M., J. (1999), Detecting Change in Categorical Data: Mining

Contrast Sets, Knowledge Discovery and Data Mining,pp.302-306

Berry, M.J.A, and Linoff,G.S (2004), Data mining techniques for marketing, sales

and customer relationship management(2nd edn), Indiana, Indianapolis publishing

Inc

128

Bolton, R. J., David J. Hand, Martin Crowder, (2004), Significance tests for

unsupervised pattern discovery in large continuous multivariate data sets,

Computational Statistics & Data Analysis Volume 46, Number 1, pp. 57 – 79

Böttcher, M., Nauck, D., Borgelt, C., & Kruse, R., ( 2006) , A framework for

discovering interesting business changes from data, BT Technology Journal ,

volume 24, issue 2,pp. 219 ¬228

Brin, S., et al., (1997), Dynamic Itemset Counting and Implication Rules for Market

Basket Data, Proc. ACM SIGMOD Int’l Conf. Management of Data, ACM Press,

New York, pp. 255¬-264.

Chen, M.C, Chiub, A.L, Chang, H.H, (2005), Mining changes in customer behavior

in retail marketing, Expert System with Applications, Volume 28, Issue 4, pp.773-

781

Cho, Y. B., Cho. Y. H., Kim, S. H., 2005, Mining changes in customer buying

behavior for collaborative recommendations, Expert Systems with Applications 28

(2005), (pp. 359–369)

Dong, G., & Li, J. (1999), Efficient mining of emerging patterns: discovering trends

and differences, Conference on Knowledge Discovery in Data archive, Proceedings

of the fifth ACM SIGKDD international conference on Knowledge discovery and

data mining, pp. 43 -52.

Dunham M.H., Xiao Y., Gruenwald Y.L., Hossain Z., (2001), a survey of

association rule mining, ACM Survey Journal (submitted), available at

http://www2.cs.uh.edu/~ceick/6340/grue-assoc.pdf

Feelders, A.J.; Daniels, H.A.M.; Holsheimer, M., (2000), Methodological and

practical aspects of datamining, Information & Management, vol.37 , pp.271-281

Han, J. & Kamber, M., (2006). Data Mining: Concepts and Techniques. San

Francisco: Morgan Kaufmann Publishers.

Hossein Javaheri, S., (2008), Response Modeling in Direct Marketing: a data mining

based approach for target selection, Master's thesis, epubl.luth.se/1653-

0187/2008/014/LTU-PB-EX-08014-SE.pdf

Kantardzic, M., (2003), Data Mining: Concepts, Models, Methods, and Algorithms

byJohn Wiley & Sons

http://www2.cs.uh.edu/~ceick/6340/grue-assoc.pdf

129

Larose, D.T. (2006) Data mining methods and model, Hoboken, New Jersey, John

Wiley & sons, Inc

Li, J., Dong, G., Ramamohanarao, K., (2000), Instance-Based Classification by

Emerging Patterns, Principles of Data Mining and Knowledge Discovery, PP.191-

200.

Li, X. B., (2005), A scalable decision tree system and its application in pattern

recognition and intrusion detection, Decision Support Systems, Volume 41, issue 1,

pp.112–130

Liu, B., Hsu, W., (1996), “Post analysis of learnt rules." AAAI-96

Liu, B., Hsu, W., Han, H. S., & Xia, Y. (2000). Mining changes for real-life

applications, Lecture Notes In Computer Science; Proceedings of the Second

International Conference on Data Warehousing and Knowledge Discovery, Volume

1874, pp. 337–346.

Liu,H.;Hussain, F.;Tan, C.L&Manornjan Dash, (2002), Discretization: An Enabling

Technique, Data Mining and Knowledge Discovery, Volume 6, Number 4, pp.393–

423

Malhotra, K.N (1996), Marketing Research: an applied orientation, india, Pearson

Education

Marcus, C. (1998), A practical yet meaningful approach to customer segmentation.

Journal of Consumer Marketing, Volume 15, issue 5, pp.494–504.

Miglautsch, J.R.,(2001), Thoughts on RFM Scoring, Journal of Database Marketing,

Volume 8, issue 1, pp. 67-72.

Min, S., H.& Han, I., (2005), Detection of the customer time-variant pattern for

improving recommender systems, Expert Systems with Applications , Volume 28,

Issue 2, pp.189–199

Nemati, H.R. & Barko, C. D., (2003), Key factors for achieving organizational data-

mining success, Industrial Management & Data Systems, Volume, 103 Issue, 4 pp.

282 -292

130

Novo, j., (2008), Drilling Down:Turning Customer Data into Profits with a

Spreadsheet, www.Jimnovo.com

Park, J.S.; Chen, M.-S. & Philip, S.Y., (1995), An Effective Hash Based Algorithm

for Mining Association Rules, Proc. International Conference on Management of

Data, Proceedings of the 1995 ACM SIGMOD international conference on

Management of data, pp.175-186.

Savasere, A. ; Omiecinski, E., & Navathe, S., (1995), An Efficient Algorithm for

Mining Association Rules in Large Databases, Proc. 21st Int’l Conf. Very Large

Data Bases, Morgan Kaufmann, San Francisco, pp. 432-444

Saunders, M., lewis, P., and Thornhill A., (2000), Research Methods for Business

Students, England, Pearson Education Limited

Silberschatz, A., & & Tuzhilin, A., (1996). What makes patterns interesting in

knowledge discovery systems? IEEE Transactions on Knowledge and Data

Engineering, 8 (6), (pp. 970-974).

Song, H. S., Kim, J. K., & Kim, S. H. (2001). Mining the change of customer

behavior in an internet shopping mall. Expert System with Applications, Volume 21,

issue 3, 157–168.

Su, J. H. & Lin, W. Y., (2004), CBW: an efficient algorithm for frequent itemset

mining , System Sciences, 2004. Proceedings of the 37th Annual Hawaii

International Conference on Volume , Issue , 5-8 Jan. 2004 Page(s): 9 pp. ¬

Thomas, S.;Bodagala, S.; Alsabti, K. & Ranka, S., (1997), An efficient algorithm for

the incremental updation of association rules in large databases, In Knowledge

Discovery and Data Mining, pp. 263-266

Toivonen, H., (1996), Sampling Large Databases for Association Rules, Very Large

Data Bases, Proceedings of the 22th International Conference on Very Large Data

Bases, pp. 134 -145

Tsai, C.,Y. & Chiu, C.-C., (2004), A purchase-based market segmentation

methodology, Expert Systems with Applications ,Volume 27, Issue 2, pp.265–276

Twocrows.com, (2005), www.twocrows.com/intro-dm.pdf

Yin, R.K, (1994), Case Study Research, design and methods (2th edn), California,

http://www.Jimnovo.com

http://www.twocrows.com/intro-dm.pdf

131

Thousand Oaks, Sage publication, Inc

Zaki, M., J.; Parthasarathy, S.; Ogihara, M. & Li, W., (1997), New Parallel

Algorithms for Fast Discovery of Association Rules, Data Mining and Knowledge

Discovery, Volume 1, Number 4, pp. 343-373.

Zhao, Q. & Bhowmick, S.S., (2003),Association Rule Mining: A Survey, Technical

Report, CAIS, Nanyang Technological University, Singapore, No. 2003116 , 200

Wu, J. & Lin, Z., (2005), Research on Customer Segmentation Model by Clustering,

ACM International Conference Proceeding Series, Proceedings of the 7th

international conference on Electronic commerce, Vol. 113, pp-316-318

Softwares:

R Software, 2007, version 2.7.0, www.r-project.com

SQL server, 2000, www.microsoft.com

http://www.r-project.com

http://www.microsoft.com

Mining Changes in Customer Purchasing Behavior - DiVA portal

Documents