To: graduate students
J. Han and M. Kamber. Data Mining: Concepts and Techniques, 2nd edition, Elsevier Inc. 2006.
Main reference books:
 P.-N. Tan, M. Steinbach, V. Kumar. Introduction to Data Mining, Addison-Wesley, 2006.
 D. Hand, H. Mannila, and P. Smyth. Principles of Data Mining, MIT Press, 2001.
(by Jiawei Han and Micheline Kamber, and can be obtained from here)
Chapter 1. Introduction
Chapter 2. Data Preprocessing
Chapter 5. Mining Frequent Patterns, Associations and Correlations
Chapter 6. Classification and Prediction
Chapter 7. Cluster Analysis
Chapter 8. Mining Stream, Time-Series and Sequence Data
Chapter 9. Graph Mining, Social Network Analysis and Multi-Relational Data Mining
Chapter 10. Mining Object, Spatial, Multimedia, Text and Web Data
Homework 1：Data mining reading notes—Mining frequent patterns
First read chapter 5 of the text book, and finish the following tasks:
(1) Find the original papers on three major frequent pattern mining methods: Apriori (Agrawal & Srikant, VLDB’94), FPgrowth (Han, Pei & Yin, SIGMOD’00) and Charm (Zaki & Hsiao, SDM’02), and write a brief note.
(2) If possible, compare running performances of the three algorithms through experiments on benchmark datasets.
(3) If possible, find 1-2 RECENT papers on the three algorithms respectively, and write a brief note to indicate the developments.
Deadline for homework 1: May 31, 2008