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 will not 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 many areas. 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. 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:
68315 , 73677 |
Credit Hours: Three |
Class Schedule: Tuesday
and Thursday, 1:40 PM - 2:25 PM |
Classroom: BYAC 260 |
Instructor: Dr. Huan
Liu |
Course Plan |
Telephone: (480) 727-7349 Email: hliu@asu.edu | TA: Mr. Jigar Mody, jigar.mody@asu.edu |
Office Hours: TTH 3:00 - 4:00pm or by appointment | TA
Office Hours: MW noon - 1:00pm, BY 214AC (inside the VLSI lab) |
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.
Topics:
Project: Students will propose a course project (either research or development type) withing the topics provided and with approval of the instructor. The evaluation of the project consists of proposal presentation, progress report, 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): Homework,
quizzes, class participation
- 25%, Paper presentation & discussion - 10%, Project - 25%
(5% proposal, 5% progress report, 5% presentation] + 10%[final
report&demo]), Exam(s) - 40%.
By Huan Liu
Drafted on December 24, 2003.
Last updated on Jan 23, 2004.