CSE 572/ CBS 572 Data Mining Spring 2009
Goals: This course will introduce basic concepts, representative algorithms, and state-of-art techniques of data mining. With the rapid advance of computer and internet technologies, a plethora of data accumulates and presents unparalleled challenges. Data will not turn into knowledge no matter how long it is kept. Mining nuggets from data will help understand problems buried in data and 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. 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, problem solving, exploration of novel ideas, 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 and insights.
This course consists of the presentations from the instructor and the participants.
Course Line Number: 18316 (CSE), 20448 (CBS) |
Credit Hours: Three, Jan 19, 2009 - May 5, 2009 |
Class Schedule: MW 5:00 - 6:15 pm |
Classroom: BYAC 260 |
Instructor: Dr. Huan Liu |
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Telephone: (480) 727-7349 Email: huan.liu at asu.edu |
TA: Mr. Xufei Alex Wang, xufei.wang@asu.edu Reza Zafarani, reza@asu.edu (Fridays, 2:00-3:00 - Office: 411 AA) |
Office Hours: MW 3:00 - 4:00pm or by appointment |
TA Office Hours: TTh: 12:30 – 1:30pm or by appointment, BYE 415 |
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:
Homework: In addition to some regular homework exercises (assignments and quizzes), students are expected to read some research papers, and present papers. The presenters are expected to conduct discussions, and answering questions.
Project: One option for this semester is for all students to work on a theme-centered project individually or in groups (?). In the past, students propose a course project (either research or development type) within the topics provided and with approval of the instructor. We will discuss the format in our first class. The evaluation of the project consists of proposal presentation, progress report, project presentation and/or demonstration, and a written report.
Textbooks: There will also be research papers and other reference
books (we will discuss about the following books and other pertinent issues. Be
there and have all your questions answered in the first class).
Introduction to Data mining
Pang-Ning Tan, Michael Steinbach, and Vipin Kumar
Addison Wesley, 2005
Data Mining: Concepts and Techniques
Jiawei Han and Micheline Kamber
Morgan Kaufmman Publishers, 2006, 2nd Edition
Data Mining: Practical
Machine Learning Tools and Techniques with JAVA
Ian H. Witten and Eibe Frank
Morgan Kaufmman Publishers, 2005, 2nd Edition
Evaluation Methods (tentative): Homework assignments – 15%, Class participation and quizzes - 5%, Paper presentation & discussion - 5%, Project – 30%, Exam(s) - 50%.
Academic Integrity
and Student Conduct
By Huan Liu
Created on January 1, 2009.
Last updated on February 11, 2009 .