Goals: This course will introduce the state-of-art techniques and representative algorithms 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 to what we are currently 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 class participation, 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) and gain unique data mining experience.
This course consists of the presentations from the instructor and
|Course Line Number:
||Credit Hours: Three|
|Class Schedule: MW 1:40
PM - 2:55 PM
||Classroom: BYAC 260|
|Instructor: Dr. Huan
|Telephone: (480) 727-7349 Email: firstname.lastname@example.org||TA:
|Office Hours: MW 3:00 - 3:30pm, 6:00 - 6:30pm or by appointment||TA
Office Hours: T: 4:30-5:30; Th: 3:10-4:10, BYE214
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.
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 other
(to be available soon on-line).
Data Mining: Concepts and Techniques
Jiawei Han and Micheline Kamber
Morgan Kaufmman Publishers, 2006
Practical Machine Learning Tools
and Techniques with JAVA
Ian H. Witten and Eibe Frank
Morgan Kaufmman Publishers, 2005, 2nd Edition
to Data mining
Pang-Ning Tan, Michael Steinbach, and Vipin Kumar
Addison Wesley, 2005
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
Created on Januray 3, 2006.
Last updated on Feb 22, 2006.