Syllabus

CS522 Advanced Database Systems

Fall 2007
Computer Science Department
California State University, Los Angeles

Lectures:

Monday and Wednesday 4:20pm - 6:00pm, in E&T A220

Instructor:

Chengyu Sun
Email: csun@calstatela.edu
Office: E&T A317
Office Hours: MW 2-4pm and F 3-5pm, or by appointment, in E&T A317

Course Description:

This is an introductory course on data mining. We will cover concepts, algorithms, and applications in data warehousing and OLAP, mining frequent patterns and association rules, classification and predication, and cluster analysis.

Prerequisites:

CS422 and CS491

Textbook(s):

Data Mining: Concepts and Techniques, by Jiawei Han and Micheline Kamber
Optional: Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, Vipin Kumar

Software Tools:

Required - Email
Optional (for access online lecture notes) - Adobe Acroread and/or MS PowerPoint

Grading Policy:

Homeworks and Projects 70%, Midterm 15%, Final 15%

90 - 100
80 - 90
60 - 80
40 - 60
below 40

A
B
C
D
F

Schedule:

Week	Topics	Chapters
1	Administrative issues Course Overview Data Warehouse and OLAP	1 3
2	Data Cube Computation	4.1-4.2
3-4	Associations	5
5-6	Classification	6
7	MIDTERM
8-9	Clustering	7
10	Anomaly Detection	7
11	FINAL

Online Resources:

Class home page - http://sun.calstatela.edu/~cysun/teaching/cs522/index.html
Class forum - http://acm.calstatela.edu/forum/viewforum.php?f=47
Web site of the text book - http://www-faculty.cs.uiuc.edu/~hanj/bk2/

Academic Integrity:

Cheating will not be tolerated. Cheating on any assignment or exam will be taken seriously. All parties involved will receive a grade of F for the course and be reported to the Academic Senate.