Goals:  This course will introduce the state-of-art techniques of data mining. 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 in science, engineering, business, medicine. Data mining is a process that finds the valuables among the mountains of data. 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 image mining, sequence mining may also be covered.

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

Course Line Number:  (main)  53092                                           Credit Hours: Three

Class Schedule: Tuesday and Thursday, 9:15AM - 10:30APM           Classroom:  SCOB 105

Instructor: Dr. Huan Liu                                                                           Course Plan

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

Office Location Hours: GWC 342, T 10:30 - 11:30am, Th 4:00-5:00pm, or by appointment

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

Topics:

• Introduction to data mining
• Classification (e.g., ID3, NBC, NN)
• Preparing data for mining (e.g., data selection, discretization, feature extraction, and instance selection)
• Clustering (e.g., numerical taxonomy, k-means, EM, DBSCAN, BIRCH)
• Data mining Applications (e.g., Image data, Customer Relationship Management, Bioinformatics)
• Mining association rules from large databases (APRIORI and its variants)
• Spatial mining
• Sequence mining
• Other mining issues 

Project: Students will perform individual course projects (either research or development type) with approval of the instructor. The evaluation of the project consists of 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

   Principles of Data  Mining
   David Hand, Heikki Mannila, and Padhraic Smyth
   MIT 2001

Evaluation Methods (tentative):  Class participation - 10%, Project Proposal, Progress Report, Presentation & discussion - 40% (15%[proposal, progress report, presentation] + 25%[final report and demo]), Exams or quizes- 50%.

Drafted on Dec. 16, 2002.
Last updated on Mar. 20, 2002.