CSE/CBS 572 Data Mining

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	Course Plan in Blackboard
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:

Background knowledge and introduction to the need and current status of knowledge discovery and data mining
Classification (e.g., ensemble methods, active learning, skewed data, cost-sensitive clustering, support vector machines)
Preparing data for mining (e.g., data selection, discretization, feature extraction, and instance selection)
Clustering (e.g., numerical taxonomy, k-means, EM, DBSCAN, BIRCH, CLIQUE, subspace clustering)
Data mining Applications (e.g., image data, streaming and sensor data, stegonography and steganalysis)
Mining association rules from large databases (APRIORI and its variants, FP-trees, multi-level association rules, maximal itemsets)
Real-world applications and case studies (e.g., Web mining, bioinformatics, text Mining, customer relationship management, online group profiling)

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 .