CS6973-002 Data Mining

Syllabus

Course Overview

This course will explore the fundamentals and the state-of-art techniques of data mining. The first half of the course will be devoted to basic concepts and techniques of data mining.We will examine issues including data warehousing and OLAP, data preprocessing, data mining languages and systems, association rule mining, classification, cluster analysis, and mining complex data. The second half of the course will explore current research issues in the area of data mining.The course will consist of a combination of activities including lectures, student presentations, class discussions, reading research literature, and course projects.

Prerequisite

Students must have graduate standing, should have a grade of B or better in at least one of the following courses: CS5443, CS5233, and CS6243 (or their equivalence), or the consent of an instructor.

Textbook

Required Textbook: Data Mining: Concepts and Techniques, by Jiawei Han and Micheline Kamber
Recommended Reference Books:

Fundamentals of Database Systems, 3rd Ed., by Elmasri & Navathe (Used for CS5443)
Readings in Database Systems, 2nd Ed., by Michael Stonebraker

here

Other reading material: Papers to read Papers to present (on-line)

Last Day to Drop

October 26, 2001.

Grading Policy

40% Project

10% Presentation

30% Homework Assignments

15% Midterm Exam

5% Intangibles

A letter grade is assigned based on accumulated numerical grade: A: over 90%, B: over 80%, C: over 70%.

Code of Honor

We believe in the highest level of academic integrity. Unless otherwise required, each student is expected to complete his or her assignment individually and independently. Although study together is encouraged, the work handed in for grading by each student is expected to be his or her own. Any form of academic dishonesty will be strictly forbidden and will be punished to the maximum extent.

Late Policy

Homework and project must be handed in by the due time. No late assignment will be accepted unless compelling reasons can be supplied and verified. Late assignments will receive no grade.

Exam Policy

All exams will be in-class, close-book and close-note. No makeup exam will be given unless compelling reasons can be supplied and verified. Missed exams will receive no grade.

Communication

A mailing list has been set up for the course. You should subscribe to the mailing list by sending an email to majordomo@ai.utsa.edu with no subject and "subscribe cs6973 me@whereami" in the body of the message, where "me@whereami" should be your email address where we can always reach you. This mailing list will be used to send important annoucements to the entire class.
The course web page is at http://ai.utsa.edu/~datamine/cs6973/f01. Assignments (homework and project), announcement, and other information regarding the course will be posted there. Students are responsible of checking out this web page frequently (at least once a week) for last-minute announcement.
The preferred time and place for the instructors to meet with students are the designated hours in their offices. In case you could not make to the office hour due to a time conflict, an appointment may be set up at a mutually agreeable time by sending the instructor an email or by a phone call.

Class Schedule (Tentative)

Color Keys:

Topics

Suggested Reading

Homework Due

Project Due

Special Events

Week of	Tuesday	Thursday
1. August 27	Introduction (Bylander) Chapter 1	Data Warehousing & OLAP (Zhang) Chapter 2
2. September 3	Data Preprocessing (Zhang) Chapter 3	Languages and System Architecture (Zhang) Chapter 4
3. September10	Concept description (Bylander) Chapter 5	Association Rules (Zhang) Chapter 6
4. September 17	Classification (Kwek) Chapter 7	Cluster Analysis (Kwek) Chapter 8
5. September 24	Association Rules (Zhang) Chapter 6.3-6.4	Association Rules (Zhang) Chapter 6.5-6.6
6. October 1	Decision Tree Classification (Bylander) Chapter 7.3 Proposal Due	Decision Tree Classification (Bylander) SLIQ and SPRINT papers Homework 1 due
7. October 8	Bayesian Classification (Bylander) Chapter 7.4	Prediction (Bylander) Chapter 7.8
8. October 15	Classifier Accuracy (Bylander) Chapter 7.9	Midterm Exam
9. October 22	Neural Networks (Kwek) Chapter 7.5	Neural Networks (Kwek) Chapter 7.5
10. October 29	Cluster Analysis (Kwek) Chapter 8.5-8.6	Cluster Analysis (Kwek) Chapter 8.7-8.9 Homework 2 due
11. November 5	Presentation: Association Mining Papers 1, 3 & 4 Progress Report due	Presentation: Classification Papers 2, 3 & 4
12. November 12	Presentation: Clustering Papers 1, 2, & 4	Presentation: Mining Sequence & Trends Papers 1, & 2 Presentaiton: Data Mining Application Papers 2 Homework 3 due
13. November 19	Presentation: Web Mining Papers 1 & 2 Presentation: Others Paper 2	Thanksgiving Holiday
14. November 26	Presentation: Visulizing Discovered Pattern Papers 1 & 2 Presentation: Data Mining Application Paper 1	Presentation: Others Paper 1 Presentation: Association Mining Paper 2
15. December 3	Presentation: Course Projects Final Report due

40%	Project
10%	Presentation
30%	Homework Assignments
15%	Midterm Exam
5%	Intangibles