CS 395: Data Mining
Syllabus

Instructor Information

Instructor: Dave Musicant
Office: CMC 326
Email: dmusican _AT_ carleton.edu
Office phone: (507)646-4369
Office hours: Mondays, 4-5, Tuesdays, 9-11, Wednesdays, 4-5

Textbook

Data Mining Introductory and Advanced Topics, Margaret H. Dunham, Prentice-Hall, 2003.

Important Dates

Take home exam 1: Assigned Monday, 4/28. Due Friday, 5/2 in class.
Take home exam 2: Assigned Friday, 5/30. Due Wednesday, 6/4 in class.
Final project due: Monday, 6/9, end of last exam.

Class Website

http://www.mathcs.carleton.edu/faculty/dmusican/cs395s03

Your Grade

Assignments: 40%
Take home exam 1: 20%
Take home exam 2: 20%
Class project: 20%

Collaboration

You are encouraged to work together, given the following ground rules:

Non-computer assignments: You should turn in your own assignment. You may work with other people, but each of you should be turning in your own.
Computer assignments: You may work together on these in pairs, if you wish. Include everyone's names in documentation at the top. Make sure to cite any ideas you get from other people, websites, books, papers, or any other references.
Take-home exams: Do these completely on your own. You can discuss them only with me.
Final project: You may do this in pairs, if you wish.

Programming Environment

You may use any programming language that you wish, so long as it is supported on our departmental machines and you provide me with ample instructions on how to compile, run, and test your code.

Homework Policy

Each assignment will have a specific time for which it will be due. An assignment turned in late within one day of the due time will be docked 25%. A program turned in later than one day of the due date but within two days will be docked 50%. An assignment turned in any time after this until the last day of classes will be docked 75%. This same policy applies to take-home exams.
College policy dictates that there can be no grace period on the final project.

Details

We will cover the following topics:

Introduction (Dunham chapters 1-3)
Classification and Regression Techniques (Dunham chapter 4 + supplemental readings)
Clustering (Dunham chapter 4 + supplemental readings)
Association Rules (Dunham chapter 5 + supplemental readings)
Web Mining (Dunham chapter 7 + supplemental readings)
Spatial Mining (Dunham chapter 8 + supplemental readings)
Temporal Mining (Dunham chapter 9 + supplemental readings)
Collaborative Filtering (supplemental readings)
Text Mining (supplemental readings)

Papers

J. Friedman. Data Mining and Statistics: What's the Connection?

N. Roussopoulos, S. Kelley, and F. Vincent. Nearest Neighbor Queries. SIGMOD 1995.

J. Gehrke, R. Ramakrishnan, and V. Ganti. RAINFOREST - A Framework for Fast Decision Tree Construction of Large Datasets. Proc. of the 24th Int'l Conference on Very Large Databases.

P. Bradley, U. Fayyad, and C. Reina. Scaling Clustering Algorithms to Large Databases. KDD 1998.

S. Guha, R. Rastogi, and K. Shim, CURE: An Efficient Clustering Algorithm for Large Databases. Information System Journal, 26:(1), 2001, Elsevier Science Ltd.

R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. Proc. of the 20th Int'l Conference on Very Large Databases.

L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank Citation Ranking: Bringing Order to the Web.

S. Chakrabarti, B. Dom, D. Gibson, J. Kleinberg, S.R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Mining the link structure of the World Wide Web. IEEE Computer, August 1999.

W. Wang, J. Yang, and R. Muntz. STING: A Statistical Information Grid Approach to Spatial Data Mining. Proceedings of the 23rd VLDB Conference.

J. Breese, D. Heckerman, C. Kadie. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.

M. Berry, S. Dumais, T. Letsche. Computational Methods for Intelligent Information Access. Proc. of the 1995 ACM/IEEE Supercomputing Conference.

Knowledge Discovery in Databases vs. Personal Privacy, symposium organized by Gregory Piatetsky-Shapiro.

CS 395: Data Mining Syllabus