Professor Harry Wechsler

Department of Computer Science

George Mason University

Fairfax, VA 22030

e-mail : wechsler@cs.gmu.edu

web : http://cs.gmu.edu/~wechsler/

(703) 993-1533 (office)

(703) 993-1530 (sec)

(703)993-1710 (fax)

GEORGE MASON UNIVERSITY

SPRING '2007

CS 484 -- DATA MINING

Class Information

001 14512 M 1:30 p.m. – 4:15 p.m. STII 15

Office Hours

M 12:30 – 1:15 PM or by appointment (SITE II - Rm. 461)

Textbook

Data Mining: Concepts and Techniques (2nd. edition), Han  and Kamber, Elsevier, 2006

web site for textbook slides  http://www-faculty.cs.uiuc.edu/~hanj/bk2/

References:

 WEKA web site for data mining software

http://www.togaware.com/datamining/survivor/Weka.html

UCI Machine Learning Repository Content Summary

http://www.ics.uci.edu/~mlearn/MLSummary.html

Course Description

Basic principles and methods for data analysis and knowledge discovery. Emphasis is on developing the basic skills needed for modeling and prediction, on one side, and performance evaluation, on the other side. Topics include system design, data quality, data preprocessing and transformation, data association, classification, clustering, biometrics, social networks and communities.

Motivation

The explosive growth in generating, collecting and storing data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge. Data mining, a multidisciplinary field, helps with the automated extraction of regularities representing knowledge implicitly stored in large databases, data warehouses, and other massive information repositories. The course focuses on issues related to the feasibility, usefulness, and efficiency of automated techniques for the discovery of patterns hidden in large databases.

Schedule

1st day of classes: January 22, 2007

Spring Break: March 12, 2007

Mid Term: March 19, 2007

Last Day of Classes: April 30, 2007

Final: May 14, 2007

Grading

Homework à 40 %

Midterm à March 19 à 20 %

Final à Monday, May 14 à 40 %

Tentative Schedule

January 22	Ch. 1: Introduction – Motivation and Functionalities; the Semantic Web (see http://www.w3.org/2001/sw/)
January 29 – February 5	Ch. 2: Data Preprocessing; Decision-Making and Pattern Recognition
February 12	Ch. 3: Data Warehouse and OLAP Technology
February 19	Ch. 4: Data Cube Computation and Data Generalization; Performance Evaluation
February 26 - March 5	Ch. 5: Mining Frequent Patterns, Associations, and Correlations; Causality -- REVIEW for MIDTERM --
March 12	Spring Break
March 19	Mid Term Exam
March 26 – April 2	Ch. 6: Classification and Prediction.
April 9 – April 16	Ch. 7: Cluster Analysis
April 23	Ch. 9.2: Social Network Analysis
April 30	Biometrics -- REVIEW for FINAL --