Introduction to Data Mining. Why Mine Data? Commercial Viewpoint Lots of data is being collected and warehoused –Web data, e-commerce –purchases at department

Introduction to Data Mining

Why Mine Data? Commercial Viewpoint• Lots of data is being collected

and warehoused

– Web data, e-commerce

– purchases at department/grocery stores

– Bank/Credit Card transactions

• Twice as much information was created in 2002 as in 1999 (~30% growth rate)

• Other growth rate estimates even higher

Largest databases in 2007• Largest database in the world: World Data Centre for Climate

(WDCC) operated by the Max Planck Institute and German Climate Computing Centre– 220 terabytes of data on climate research and climatic trends,

– 110 terabytes worth of climate simulation data.

– 6 petabytes worth of additional information stored on tapes.

• AT&T– 323 terabytes of information

– 1.9 trillion phone call records

• Google– 91 million searches per day,

• After a year worth of searches, this figure amounts to more than 33 trillion database entries.

Why Mine Data? Scientific Viewpoint

• Data is collected and stored at enormous speeds (GB/hour). E.g.

– remote sensors on a satellite

– telescopes scanning the skies

– scientific simulations generating terabytes of data

• Very little data will ever be looked at by a human

• Knowledge Discovery is NEEDED to make sense and use of data.

Data Mining• Data mining is the process of automatically discovering useful

information in large data repositories.

• Human analysts may take weeks to discover useful information.

• Much of the data is never analyzed at all.

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

1995 1996 1997 1998 1999

The Data Gap

Total new disk (TB) since 1995

Number of analysts

From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”

What is (not) Data Mining?

What is Data Mining?

– Certain names are more prevalent in certain locations (O’Brien, O’Rurke, O’Reilly… in Boston area)

–Discover groups of similar documents on the Web

What is not Data Mining?

– Look up phone number in phone directory

– Query a Web search engine for information about “Amazon”

• Draws ideas from: machine learning/AI, statistics, and database systems

Origins of Data Mining

Machine LearningStatistics

Data Mining

Database systems

Data Mining TasksData mining tasks are generally divided into two major categories:

• Predictive tasks [Use some attributes to predict unknown or future values of other attributes.]

• Classification

• Regression

• Deviation Detection

• Descriptive tasks [Find human-interpretable patterns that describe the data.]

• Association Discovery

• Clustering

Predictive Data Mining or Supervised learning

• Given a collection of records (training set)– Each record contains a set of attributes, one of the attributes is the class.

• Find ("learn") a model for the class attribute as a function of the values of the other attributes.

• Goal: previously unseen records should be assigned a class as accurately as possible.

LearningWe can think of at least three different problems being involved in

learning:

• memory,

• averaging, and

• generalization.

Example problem(Adapted from Leslie Kaelbling's example in the MIT courseware)

• Imagine that I'm trying predict whether my neighbor is going to drive into work, so I can ask for a ride.

• Whether she drives into work seems to depend on the following attributes of the day:– temperature,

– expected precipitation,

– day of the week,

– what she's wearing.

Memory• Okay. Let's say we observe our neighbor on three days:

ClothesShopDayPrecipTemp

WalkCasualNoSatNone25

DriveCasualYesMonSnow-5

WalkCasualYesMonSnow15

Memory• Now, we find ourselves on a snowy “–5” – degree Monday, when

the neighbor is wearing casual clothes and going shopping.

• Do you think she's going to drive?

Temp Precip Day Clothes

25 None Sat Casual Walk

-5 Snow Mon Casual Drive

15 Snow Mon Casual Walk

-5 Snow Mon Casual

Memory• The standard answer in this case is "yes".

– This day is just like one of the ones we've seen before, and so it seems like a good bet to predict "yes."

• This is about the most rudimentary form of learning, which is just to memorize the things you've seen before.




15 Snow Mon Casual Walk


Noisy Data• Things aren’t always as easy as they were in the previous case. What

if you get this set of noisy data?




25 None Sat Casual Drive





25 None Sat Casual ?

• Now, we are asked to predict what's going to happen.

• We have certainly seen this case before.

• But the problem is that it has had different answers. Our neighbor is not entirely reliable.

Averaging• One strategy would be to predict the majority outcome.

– The neighbor walked more times than she drove in this situation, so we might predict "walk".










Generalization• Dealing with previously unseen cases

• Will she walk or drive?


22 None Fri Casual Walk

3 None Sun Casual Walk

10 Rain Wed Casual Walk

30 None Mon Casual Drive

20 None Sat Formal Drive



27 None Tue Casual Drive

24 Rain Mon Casual ?

• We might plausibly make any of the following arguments:

– She's going to walk because it's raining today and the only other time it rained, she walked.

– She's going to drive because she has always driven on Mondays…

Classification Another Example

Tid Refund MaritalStatus

TaxableIncome Cheat

1 Yes Single 125K No

2 No Married 100K No

3 No Single 70K No

4 Yes Married 120K No

5 No Divorced 95K Yes

6 No Married 60K No

7 Yes Divorced 220K No

8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes10

categoric

al

categoric

al

continuous

class

Refund MaritalStatus

TaxableIncome Cheat

No Single 75K ?

Yes Married 50K ?

No Married 150K ?

Yes Divorced 90K ?

No Single 40K ?

No Married 80K ?10

TestSet

Training Set

ModelLearn

Classifier

Example of a Decision Tree

Tid Refund MaritalStatus

TaxableIncome Cheat

1 Yes Single 125K No

2 No Married 100K No

3 No Single 70K No

4 Yes Married 120K No

5 No Divorced 95K Yes

6 No Married 60K No

7 Yes Divorced 220K No

8 No Single 85K Yes

9 No Married 75K No

10 No Single 90K Yes10

categoric

al

categoric

al

continuous

class

Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No

Married Single, Divorced

< 80K > 80K

Splitting Attributes

Training Data Model: Decision Tree

Apply Model to Test Data

Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No


< 80K > 80K

Refund Marital Status

Taxable Income Cheat

No Married 80K ? 10

Test DataStart from the root of tree.


Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No


< 80K > 80K



No Married 80K ? 10

Test Data


Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No


< 80K > 80K



No Married 80K ? 10

Test Data


Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No


< 80K > 80K



No Married 80K ? 10

Test Data


Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No


< 80K > 80K



No Married 80K ? 10

Test Data


Refund

MarSt

TaxInc

YESNO

NO

NO

Yes No


< 80K > 80K



No Married 80K ? 10

Test Data

Assign Cheat to “No”

Classification: Direct Marketing– Goal: Reduce cost of mailing by targeting a set of consumers likely

to buy a new cell-phone product.

– Approach:• Use the data for a similar product introduced before.

• We know which customers decided to buy and which decided otherwise. This {buy, don’t buy} decision forms the class attribute.

• Collect various demographic, lifestyle, and other related information about all such customers. E.g.

• Type of business,

• where they stay,

• how much they earn, etc.

• Use this information as input attributes to learn a classifier model.

Classification: Fraud Detection• Goal: Predict fraudulent cases in credit

card transactions.• Approach:

• Use credit card transactions and the information associated with them as attributes, e.g.

– when does a customer buy, – what does he buy, – where does he buy, etc.

• Label some past transactions as fraud or fair transactions. This forms the class attribute.

• Learn a model for the class of the transactions.

• Use this model to detect fraud by observing credit card transactions on an account.

Classification: Attrition/Churn• Situation: Attrition rate for mobile

phone customers is around 25-30% a year!

• Goal: To predict whether a customer is likely to be lost to a competitor.

• Approach:• Use detailed record of transactions with

each of the past and present customers, to find attributes. E.g.

– how often the customer calls,

– where he calls,

– what time-of-the day he calls most,

– his financial status,

– marital status, etc.

• Label the customers as loyal or disloyal. Find a model for loyalty.

Success story (Reported in 2003):• Verizon Wireless performed

this kind of data mining reducing attrition rate from over 2% per month to under 1.5% per month.

• Huge impact, with >30 M subscribers (0.5% is 150,000 customers).

Assessing Credit Risk• Situation: Person applies for a loan

• Task: Should a bank approve the loan?– People who have the best credit don’t need the loans

– People with worst credit are not likely to repay.

– Bank’s best customers are in the middle

• Banks develop credit models using a variety of data mining methods.

• Mortgage and credit card proliferation are the results of being able to "successfully" predict if a person is likely to default on a loan.

• Widely deployed in many countries.

Frequent-Itemset Mining (Association Discovery)

The Market-Basket Model

• A large set of items, e.g., things sold in a supermarket.

• A large set of baskets, each of which is a small set of the items, e.g., the things one customer buys on one day.

Fundamental problem

• What sets of items are often bought together?

Application

• If a large number of baskets contain both hot dogs and mustard, we can use this information in several ways. How?

Hot Dogs and Mustard1. Apparently, many people walk from where the hot dogs are to

where the mustard is. • We can put them close together, and put between them other foods

that might also be bought with hot dogs and mustard, e.g., ketchup or potato chips.

• Doing so can generate additional "impulse" sales.

2. The store can run a sale on hot dogs and at the same time raise the price of mustard. • People will come to the store for the cheap hot dogs, and many will

need mustard too.

• It is not worth the trouble to go to another store for cheaper mustard, so they buy that too.

• The store makes back on mustard what it loses on hot dogs, and also gets more customers into the store.

Beer and Diapers• What’s the explanation here?

On-Line Purchases• Amazon.com offers several million different items for sale, and

has several tens of millions of customers.

• Basket = Customer, Item = Book, DVD, etc. – Motivation: Find out what items are bought together.

• Basket = Book, DVD, etc. Item = Customer – Motivation: Find out similar customers.

Words and Documents• Baskets = sentences; items = words in those sentences.

– Lets us find words that appear together unusually frequently, i.e., linked concepts.

• Baskets = sentences, items = documents containing those sentences.– Items that appear together too often could represent plagiarism.

Genes• Baskets = people; items = genes or blood-chemistry factors.

– Has been used to detect combinations of genes that result in diabetes

Clustering• Given a set of data points, each having a set of attributes, and a

similarity measure among them, find clusters such that– Data points in one cluster are more similar to one another.– Data points in separate clusters are less similar to one another.

• Similarity Measures:– Euclidean Distance if attributes are continuous.– Other Problem-specific Measures.

E.g. Euclidean Distance Based Clustering in 3-D space.

Intracluster distancesare minimized

Intracluster distancesare minimized

Intercluster distancesare maximized

Intercluster distancesare maximized

Clustering: Application 1• Market Segmentation:

– Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix.

– Approach: • Collect different attributes of customers based on their

geographical and lifestyle related information.

• Find clusters of similar customers.

Clustering: Application 2• Document Clustering:

– Goal: To find groups of documents that are similar to each other based on the important words appearing in them.

– Approach: • Identify frequently occurring words in each document. • Form a similarity measure based on the frequencies of different

terms. Use it to cluster.– Gain: Information Retrieval can utilize the clusters to relate a new

document to clustered documents.

Each article is represented as a set of word-frequency pairs (w, c).

There are two natural clusters in the data set. The first cluster consists of the first four articles, which correspond to news about the economy.The second cluster contains the last four articles, which correspond to news about health care.

Introduction to Data Mining. Why Mine Data? Commercial Viewpoint Lots of data is being collected and warehoused –Web data, e-commerce –purchases at department

Documents

data mining data mining

data mining slide

terabytes of data

little data

predictive data mining

scientific viewpoint

world data centre

warehoused web data