CSE 572 Data Mining    Spring 2011

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 new challenges. Data will not turn into knowledge no matter how long it is kept. Mining nuggets from data will help understand patterns 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 theories behind them, and explore new and improved techniques for real world data mining applications. The course is arranged to encourage active class participation, creative thinking, practical 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 given 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/or from the participants.

Course Line Number:  14591

Credit Hours: Three, Jan 18, 2011 - May 3, 2011

Class Schedule: TTh 9:00 - 10:15 am

Classroom:  BYAC 190

Instructor: Dr. Huan Liu  

Course Plan in Blackboard

Telephone:  (480) 727-7349 Email: huan.liu at asu.edu  

TA:   Mr. Ali Abbasi, mabbasi2 at asu.edu

  Mr. Reza Zafarani, reza at asu.edu (F 1-2pm, by appointment only);

  Mr. Geoff Barbier, gbarbier at asu.edu (Th 10-11am, by appointment only)

Office Hours: TTh 10:15 - 11:15am or by appointment

TA Office Hours: MW 4:00 - 5:00 pm, BYE 214 or by appointment


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.



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 – 15-20%, Class participation and quizzes - (extra credit, up to 5%), Paper presentation & discussion - 5%, Project – 20-30%, Exam(s) - 40-50%.

Academic Integrity and Student Conduct

By Huan Liu

Created on December 31, 2010.
Last updated on January 19, 2011.