Kent State University 
CS 63015/73015: Data Mining Techniques 
Fall 2008

Instructor: Ruoming Jin

Mondays 5:30PM-8:15PM; Rm. MSB 276
Office Hours (MSB 264): Mondays 4:00PM-5:00PM

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Prerequisites

CS 4/56101 Algorithms                                                                                                      CS 4/53005 Introduction to Database Systems
CS 33001 Data Structures                                                                                                      or Consent of the Instructor

Course Overview

This course teaches the fundamental concepts and techniques of data mining. We will cover a set of interesting topics, including pattern discovery/association rule mining, clustering, classification, information theory, decision theory/Bayesian inference, graphical models, kernel methods/support vector machine, spectral clustering, semi-supervised learning, etc. 

Each student will be expected to present a paper and lead the discussion following his/her presentation and do a project on selected topics. There will be neither homework nor exam. There will be two or three in-class exercise-sessions.

Textbook

P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, 2005.

Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006

Other references:

[1] Data Mining --- Concepts and techniques, by Han and Kamber, Morgan Kaufmann, 2001. (ISBN:1-55860-489-8)
[2] Principles of Data Mining, by Hand, Mannila, and Smyth, MIT Press, 2001. (ISBN:0-262-08290-X)
[3] The Elements of Statistical Learning --- Data Mining, Inference, and Prediction, by Hastie, Tibshirani, and Friedman, Springer, 2001. (ISBN:0-387-95284-5)
[4] Mining the Web --- Discovering Knowledge from Hypertext Data, by Chakrabarti, Morgan Kaufmann, 2003. (ISBN:1-55860-754-4)

[5] I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005.

Additional materials will include papers supplied by the instructor

Requirements & Grading Policy

A student's grade will be determined as a weighted average of project (40%), class participation (20%), and presentation (40%).

Lectures

Ø 9/8/08: Frequent Itemset Mining

Ø 9/15/07: Association Rule Mining, Performance and Scalability Issues for FIM

Ø 9/22/08: Advanced Frequent Pattern Mining

Ø 9/29/08, 10/6/08: Clustering

Ø 10/13/08: Clustering Validation

Ø 10/13/08: Classification (Decision-Tree Construction)

Ø Text Mining Presentations

1. Chapter 1

2. Chapter 2 (part-1), (part 2)

3. Chapter 3

4. Chapter 4 (part-1), (part2)