CSE 591d Data Mining

Goals:  This course will introduce the state-of-art techniques of data mining and data warehousing. With the rapid advance of computer and internet technologies, a plethora of  data accumulates. Data won't turn into knowledge no matter how long it is kept. If we can mind nuggets from data, this would add values and edges to what we are doing. Data mining is a process that finds the valuables among the mountains of data, and data warehousing is a process that organizes huge databases for data mining and OLAP. We will review and examine the present techniques and the theories behind them and explore new and improved techniques for real world data mining applications. The arrangement of the course will encourage active discussion, creative thinking, and hands-on project development among the participants. A course project on some specific aspect of this emerging field will be required for each student to explore some in-depth issue(s). Related topics on XML and Metadata, Web Mining will also be covered.

This course consists of the presentations from the instructor and the participating students.

Course Line Number:  88935                                                        Credit Hours: Three

Class Schedule: Tuesday and Thursday, 3:15 PM - 4:30 PM           Classroom:  NUR004 (LL271 from Jan 17, 2002)

Instructor: Dr. Huan Liu                                                                                    Course Plan

Telephone:  (480) 727-7349                                                                            Email: hliu@asu.edu

Office Hours: TTH 4:30 - 5:45pm or by appointment

Prerequisite: Introduction to Artificial Intelligence (CSE471 or CSE598) or Introduction to Database Management Systems (CSE412), some system development experience related to data engineering and handling, or consent from the instructor.


Homework: Students will read research papers and chapters in the reference books, present papers in  groups or individually. The presenters will also be responsible for conducting group discussions and answering questions.

Project: Students will propose a course project (either research or development type) with approval of the instructor. The evaluation of the project consists of proposal presentation, project presentation and/or demonstration and a written report.

Textbook: There will also be research papers and reference books (to be available soon on-line).
    Data Mining: Concepts and Techniques
    Jiawei Han and Micheline Kamber
    Morgan Kaufmman Publishers, 2000

    Data Mining: Practical Machine Learning Tools and Techniques with JAVA
   Ian H. Witten and Eibe Frank
   Morgan Kaufmman Publishers, 2000

Evaluation Methods (tentative)Class participation - 10%, Topic Presentation & discussion - 25%, Project - 25% (10%[presentation] + 15%[final report&demo]), Exams - 40%.

Drafted on November 29, 2001.
Last updated on Jan 15, 2002.