Web Mining and Visualization for E-Commerce Presented By Vandana Janeja.

Web Mining and Visualizationfor E-Commerce

Presented By

Vandana Janeja

Presentation Outline

Website Usage Data JDK1.3, JavaScript, Java Servlets, Java based

web servers, Database MS Access

Data Mining Algorithms- K-Means, Apriori, Text Mining

Visualization for Website management Java3D, JDK1.3

Outline

Gather Data

Analyze Data

Visualize Data

•Web Crawler•Servlets- For Server side Data•JavaScript and Java Programs - for Client side data

•Data Mining•Text Mining•Clustering

•Decision Support System-Reporting System

•Java3D Visualization Algorithm• Simulation Programs

Web Site Management

Client Side Server Side

Web Site Reading Component

Matrix Structure

3D Representation of Static Web Site

Encrypted Data

User Tracking and Log File Reading Components

Matrix Structure

3D Representation of Usage Of Web Site

Encrypted Data

Other Server side components like web siteRemediation

Model

Gather Data

Analyze Data

Visualize Data

•Web Crawler•Servlets- For Server side Data•JavaScript and Java Programs - for Client side data•Collaboration




Data Gathering

Application Server

Users

Browser

User Log Files

+Info from Programs

Server Side Programs

Data Base Data storage

Client Side Programs

Data mining

WEB SITE Static Site Map•http://www.library.njit.edu/etd/njit-mt2001-010/thesis.html

Usage Map: http://www.visualinsights.com

http://www.library.njit.edu/etd/njit-mt2001-010/thesis.html









http://www.visualinsights.com/



Reports

Host PingedHost names >1 hit

Host names

Intermediary Hosts Along connection path

Results of host pinging(done 4x’s per day)

Host names >1 hit

Host Traced

UsageDB Database

Table:UsageDataTable

Table:RouterInfo

Table:PrefRouterInfo

Table:Cookies

Table:UserAgent

Tables:Url; Scripts; Meta;

Applets; ...

Input Data:Servlet data

Input Data:Javascript data

Usage Database

Input Data:Client side

website parsing

Gather Data

Analyze Data

Visualize Data





Outline

Visualization

The objective of the project was to develop a 3-Dimensional (3-D) visualization tool from an adjacency matrix representing connectivity between elements and usage of connectivity paths between these elements.

The visualization of connectivity could be for elements like routers and websites.

Web Crawler Web Site Link Reader******************

Matrix Structure

Index.html

Url1

Url2

Url3

Url1.html

Url4

Url5

Url6

URL2.html

Url7

Url8

Url9

Url3.html

Url10

Url11

Url12

1

2

3

5

96

78

4Adjacency Matrix:

1 : [2,3,4]2 : [5]3 : [6]4 : [7,8,1]5 : [1]6 : [9]7 : []8 : []9 : []

1 2 3 4 5 6 7 8 9

1 1 1 1 1 0 0 0 0 0

2 0 1 0 0 1 0 0 0 0

3 0 0 1 0 0 1 0 0 0

4 1 0 0 1 0 0 1 1 0

5 1 0 0 0 1 0 0 0 0

6 0 0 0 0 0 1 0 0 1

7 0 0 0 0 0 0 1 0 0

8 0 0 0 0 0 0 0 1 0

9 0 0 0 0 0 0 0 0 1

Web Page

Connectivity/ Hyperlink

Example 2:

Adjacency Matrix:1 : [2,6]2 : [3,7]3 : [4,8]4 : [5,9]5 : [1,10]6 : [8]7 : [9]8 : [10]9 : [6]10: [7]

Generating the N x N Gmatrix For Peterson’s Graph: 1 2 3 4 5 6 7 8 9 10

1 0 1 0 0 0 1 0 0 0 0

2 0 0 1 0 0 0 1 0 0 0

3 0 0 0 1 0 0 0 1 0 0

4 0 0 0 0 1 0 0 0 1 0

5 1 0 0 0 0 0 0 0 0 1

6 0 0 0 0 0 0 0 1 0 0

7 0 0 0 0 0 0 0 0 1 0

8 0 0 0 0 0 0 0 0 0 1

9 0 0 0 0 0 1 0 0 0 0

10 0 0 0 0 0 0 1 0 0 0

Adjacency Matrix:

1 : [2,6]2 : [3,7]3 : [4,8]4 : [5,9]5 : [1,10]6 : [8]7 : [9]8 : [10]9 : [6]10: [7]

3D Representation as a cylinder

1 2 3 4 5 6 7 8 9

1 1 1 1 1 0 0 0 0 0

2 0 1 0 0 1 0 0 0 0

3 0 0 1 0 0 1 0 0 0

4 1 0 0 1 0 0 1 1 0

5 1 0 0 0 1 0 0 0 0

6 0 0 0 0 0 1 0 0 1

7 0 0 0 0 0 0 1 0 0

8 0 0 0 0 0 0 0 1 0

9 0 0 0 0 0 0 0 0 1

Possible Applications

Ad Placement Network Diagnostic Collaboration Detecting Anomalies

Measuring viewer usage is done in an indirect fashion. The advantage of Internet advertising is increased feedback to advertisers though the use of greater levels of interactivity, targeting and precise measurement of user behavior.Various pricing models used for currently in use are:

cost per thousand (and a related mechanism, flat fee /sponsorship); click through(CPM, CPC, CPL); hybrid models; outcomes.

Cost Per Thousand and Flat Fee /Sponsorship

One Look at the BANNER = 1 Impression

Cost Of Advertisement

1000 Impressions

Higher Traffic Higher CPM

Factors:Usage Traffic Profiles

Network Diagnostic

UsageDatabase

Most preferred User report

RouterList

Connectiv ity Program

Connectiv ityDatabase

«process»Generates

Time /Date

HistoryCheck

ResponseIndex

<<input>>

<<input>>

UML Model of Network Diagnostic

Collaboration

Website Collaboration based on Affiliate Model

Web Site AWeb Site B

Exit point

Entry Point & source

Web site A Web site B Web site C

Central Database

User crosses over to Site B and a complete dataset of the users activity at web site A is passed to web site B and so on.

The consolidated datasets of transactions of the user across web sites are written to a central database

1. Consolidated central schema

Web site A Web site B Web site C

Distributed Central Database: This database is the same database for all web site but it could be available in the form of distribuited elements to each web site

2. Cooperating central schema

Web SiteA

URL 1A

Web SiteB

URL 1BSessionID as URL rewriting

To be able to pass Session id for single window scenario(where the link appears on the URL).

Web SiteA

URL 1A

Web SiteB

URL 1B

SessionID in a bean along with other data

1> object pool for multiple windows - the object containing the entire data about the session passed as a bean to the collaborating site,

Web SiteA

URL 1A

Web SiteB

URL 1B

Cookie Table in Shared Pool

1> cookies for multiple windows with a cookie table in the shared pool,Here both collaborating sites can access the cookies for both web sites.

Web SiteA

URL 1A

Web SiteB

URL 1B

DatabaseA

LogFile SiteA from Servlet programs

LogFile SiteB from Servlet programs

DatabaseB

Query with Join = Temporary Table

Collaboration Reports

1> Table for an entire log file(generated by Servlet programs) along with Session Id for each user which can be used either as a shared pool or as an element in a join query on the databases : for eg :select * from SiteATable,SiteBTable where SiteATable.SessionID= SiteBTable.SessionID

Gather Data

Analyze Data

Visualize Data





Outline

Text Mining and Association Rule Mining on the web

Some Types of Text Data Mining

Keyword-based association analysis Similarity detection

Cluster documents by a common author Cluster documents containing information from a

common source

Link analysis: unusual correlation between entities

Anomaly detection: find information that violates usual patterns

Test Case : njit.edu List of pages traversed

HTML Text Of pages traversedKeyword list after pruning

Count of keywords for each HTML page

Sample Apriori Rules

3 <- 2 (70.0%, 85.7%)2 <- 3 (70.0%, 85.7%)2 <- 1 (60.0%, 83.3%)4 <- 5 (30.0%, 100.0%)3 <- 2 1 (50.0%, 80.0%)2 <- 3 1 (40.0%, 100.0%)4 <- 3 5 (10.0%, 100.0%)4 <- 1 5 (10.0%, 100.0%)2 <- 3 4 1 (20.0%, 100.0%)

Mining Association Rules—An Example

For rule A C:support = support({A C}) = 50%

confidence = support({A C})/support({A}) = 66.6%

The Apriori principle:Any subset of a frequent itemset must be frequent

Transaction ID Items Bought2000 A,B,C1000 A,C4000 A,D5000 B,E,F

Frequent Itemset Support{A} 75%{B} 50%{C} 50%{A,C} 50%

Min. support 50%Min. confidence 50%

Reference: http://www.cs.sfu.ca/~han/DM_Book.html

Data MiningClustering Using K-Means

K-Means the clusters are formed based on the basis of distance from a centroid

•K-means cluster analysis. K-means cluster analysis uses Euclidian distance. •Initial cluster centers are chosen in a first pass of the data, then each additional iteration groups observations based on nearest Euclidian distance to the mean of the cluster. •Thus cluster centers change at each pass. •The process continues until cluster means do not shift more than a given cut-off value or the iteration limit is reached.

The K-Means Clustering Method

1.Test Case: 0 - 2,3 1 - 4,5

•Test Case: 0 – 2,6 1 – 4,5

But what if the number of clusters changes•Test Case : 0 – 3, 5 (Case in which K changes ) 1 – 6, 2

Text Mining and Visualization:

• The web site is inherently made up with a directory structure, which is essentially a tree structure. This is a kind of inherent similarity based grouping; All the related pages are kept in a directory.

• The web pages can also be grouped or clustered together based on other similarity features which can be generated by text mining.

• All the web pages can be similar to each other by the appearance of certain keywords in them. These can be extracted and pruned using certain text mining algorithms. Once this is done the web pages can be logically grouped in such a way that it will be a “Bottom Up Approach” a set of pages can be input into the text mining engine. This engine can come up with the most similar pages based on appearance of keywords (which are also gathered using an algorithm).

• This engine works on each directory and subdirectory structure. Subsequently “X” such web pages can be grouped together. This will form a hierarchy of sets of “X” pages arranged in a hierarchy.

Individual Pages clustered based on a similarity measure

Cluster of “X” such pages at the same level based on the similarity measure

Highest level with a cluster of clusters

Cylinder Visualization of Very Large Sites

Putting It all together

Data Gathered from Different sources

Mining

Visualization

Mining Result

References: Sudipto Guha, R.Rastogi, K.Shim :A clustering algorithm for categorical attributes. Technical report, Bell

laboratories, Murray Hill 1997 ROCK : A Robust Clustering Algorithm for Categorical Attributes: Sudipto Guha, Rajeev Rastogi, Kyuseok

Shim. Published in the Proceedings of the IEEE Conference on Data Engineering, 1999Discussion on K-Means

R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. Wiley, New York, 1973. [16] O. Egecioglu and H. Ferhatosmanoglu. Circular data-space partitioning for similarity queries and

parallel disk allocation. In Proc. of IASTED International Conference on Parallel and Distributed Computing and Systems, pages 194-200, November 1999.

• A.K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988. J. MacQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of

the Fifth Berkeley Symposium on Math. Stat. and Prob, volume 1, pages 281-196, 1967. http://www.cs.sfu.ca/~han/DM_Book.html J. Pei, J. Han, and R. Mao. CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets.

DMKD'00, Dallas, TX, 11-20, May 2000.

R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. KDD'97, 67-73, Newport Beach, California.

H. Toivonen. Sampling large databases for association rules. VLDB'96, 134-145, Bombay, India, Sept. 1996.

http://www.cs.sfu.ca/~han/DM_Book.html

Acknowledgements and Disclaimers

Advisors: Dr.Manikopoulos

Associate Professor,Electrical and Computer Engineering Department, New Jersey Institute of Technology

Dr.Jay Jorgenson Professor, Mathematics Department,City University Of New York

Software Development team at Network Security Solutions: Some of the material is a copyright of NSS,Inc and SiteGain,Inc.

Thesis in visualization was done during the Master’s at NJIT

Web Mining and Visualization for E-Commerce Presented By Vandana Janeja.

Documents

data javascript

servlet data input data

programs data mining

data web crawler servlets

web mining

visualization slide

web site remediation

programs server