CSE572 Data Mining

Data Mining (MW 4:40-5:30P, BYAC 240)

Course Summary

Welcome to my personal CSE572 wiki. My findings on data mining will be posted at this wiki.

Course Information

Huan Liu: www, email, 480-727-7349 (O)

Office Hours: MW 3:30-4:00P, 6:00-6:30P, or by appointment

TA: Zheng Zhao

TA Office Hours: T: 4:30-5:30; Th: 3:10-4:10, BYE214

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, adversary classification)
• 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)

Homework

Homework exercises include assignments and quizzes. Students are expected to read, present, and discuss research papers.

Project

Propose a course project (either research or development type) and get approval from instructor. The evaluation of the project consists of

• proposal presentation
• progress report
• project presentation and/or demonstration
• and a written report

Textbooks

Several books are recommended, though you likely only need, or rather, can only read, one.