Executive Software Engineering Program

EE382C –Data Mining

Spring 2003

Instructor:

Joydeep Ghosh, Ph.D. Professor

Email address: ghosh@ece.utexas.edu;

URL: http://www.lans.ece.utexas.edu/~ghosh

Course Title and Description:

Many companies that gather huge amounts of electronic data have now begun applying data mining techniques to their data warehouses to discover and extract “hidden” patterns useful for making smart business decisions. Effective data mining requires an understanding of concepts from exploratory data analysis, pattern recognition, machine learning/ AI, heterogenous data bases, parallel processing and data visualization, in addition to knowing the application domain. I will focus on basic techniques for data mining, including methods useful for analyzing information from the world wide web. Demos using an industrial strength software (SAS) as well as a public domain JAVA package (WEKA) will be given and some applications/case studies will be discussed. The course involves a mid-term exam, a paper presentation and a term project. There will be no final exam.

Textbook(s):

Title: Data Mining: Concepts and Techniques

Author Han and Kamber (HK)

Publisher Morgan Kaufmann

ISBN: 1-55860-489-8

Title: Data Mining

Author: Witten and Frank (WF)

Publisher: Morgan Kaufmann

ISBN: 1-55860552-5

Course Expectations:

This course requires students to have very basic knowledge of JAVA. An undergraduate level understanding of probability/statistics, data analysis, databases and linear algebra is assumed. This is a graduate course so the workload will be medium to heavy.

While studying techniques for database representation/modeling, clustering, classification, finding associations and sequence processing, emphasis will be placed on the issues of algorithm scalability, performance, interpretability and the ability to deal with garbage data. 10-15 minute student talks will be interwoven with the lectures, depending on class size. The last two classes will largely consist of student term-project presentations, followed by active discussion.

Class outline:

January 24 and 25 -- Introduction

Reading Assignment: HK ch1-3; WF ch 1, 2

Area of study: overview, SAS demos, data warehousing, OLAP; Data quality and pre-processing

February 21 and 22

Reading Assignment:: HK ch5, 6, 8 ; WF 3, 4.5, 6.6; 7

Area of study: clustering/segmentation; market basket analysis; intro to finding association rules

March 21 and 22

Reading Assignment:: HK ch 7; WF rest of Ch 4-7.

Area of study: Assoc. Rules (contd), classification; prediction/ forecasting

April 11 and 12

Reading Assignment: from papers/notes

Area of study: combining multiple models; web analytics: analyzing hyperlink structure and content of websites.

May 9 and 10

Reading Assignment: HK ch 9; notes

Area of study: web analytics (contd): analyzing usage of web sites.; project presentations; course wrap-up; the future of data mining.

Grading Information:

45% final project,

20% written homework,

20% mid-term

10% brief presentation of research paper (groups of 2)

5% class participation