CSE 572 DATA MINING

(January 16 – May 1, Spring 2007)

I hear, I forget; I see, I remember; I do, I understand. - Proverb

We will work together to hear, see and do in this class in many forms including lectures, invited talks, discussions, research paper reading assignment, a project, and presentation, in addition to homework, quizzes, and exam(s).  We will create opportunities to learn from each other. If you find anything interesting about data mining, please share with us all. The ultimate goal is to provide a conducive environment that unleashes the student creativity in producing new works with impact.

We want to strike a balance between exploiting and exploring in learning with respect to passive and active learning.

Your suggestions are most welcome. Please send email to huan.liu at asu.edu

OUTLINE

1. Introduction to Data Mining
2. Classification Methods (ensemble methods, SVMs, skewed data, cost-sensitive classification)
3. A Brief Review of Probability and Entropy
4. Performance Evaluation (Measures, Comparison between two algorithms)
5. Data, Data Preparation, and Data Preprocessing (feature selection, discretization, sampling, instance selection)
6. Clustering Methods (subspace clustering, CLIQUE)
7. Association Rules
8. Current Challenges
1. The Shark Toothed Elephant – an invited talk by Bill Rose, VP GIS of Avnet
2. USuggest  - an invited talk on data mining in a Web application
9. Some Thought-Provoking Applications (steganography and steganalysis, streaming data extraction, gene selection)

ASSIGNMENTS (To be updated)

• Please check Assignments at myASU.
• There is an extra credit (up to 10%) for class participation and discussion.
• Three to five homework assignments.
• Research paper reading assignment, and selected presentations (We will discuss more in class)
• Everyone will need to work on a course project.
• A theme-based project for all in class
• Project final report and demo

More on Paper Reading Assignment, Project and its Due Dates

EXAMS

• 2 exams: First (mid-term) exam is on March 5, Monday

LINKS

• KDNuggets - a comprehensive site for many resources of KDD
• KDD Cup 2001 - included here as an example, you can find many new ones at SIGKDD conference sites.
• Tutorial on Feature Selection for Data Mining
• Tutorial on Mining and Indexing of Time Series Data under Research Information
• Data mining software packages recommended by Chris Mayer
• Random Forests by Leo Breiman
• Bioinformatics Journal, Oxford University Press, available to ASU students
• FAQ's

Prepared by Huan Liu on January 9, 2007
Last updated: April 17, 2007