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).

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

Course Line Number:  66554                                                         Credit Hours: Three

Class Schedule: Monday and Wednesday, 1:40 PM - 2:55 PM            Classroom:  SCOB 302

Instructor: Dr. Huan Liu                                                                 Course Plan (Fall 2000)

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

Office Hours: Monday and Wednesday 3:00 - 4:00pm or by appointment

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

Topics:

• Introduction to knowledge discovery and data mining
• Classification (ID3, NN)
• Preparing data for mining (data selection, discretization, feature selection)
• Clustering (numerical taxonomy, k-means, COBWEB)
• Data mining Applications
• Mining association rules from large databases
• Data warehousing: issues, solutions, implementation
• Database schema, data marting, metadata
• Mining issues related to the Web

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 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:  Class participation - 10%, Presentation & discussion -20%, Project - 35% (5%[proposal] + 10%[presentation] + 20%[final report&demo]), Quizzes - 35%