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CS 391A/691AG - Data Mining
Fall Semester 2006
 

TextBook: Introduction to Data Mining
by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar (Addison Wesley, 2006); ISBN: 0-321-32136-7.

Prerequisites: Programming experience with languages such as C, C++, and Java.
Scripting languages such as Perl and Python are quite beneficial also. Some numerical analysis and/or statistics is desirable.

Students with disabilities that require an accommodation for taking this course should contact the Learning Assistance Center
(758-5929) within the first two weeks of the semester.

Grades: Points are awarded throughout the term for performance on selected homework problems (25%), the midterm (25%), the course project (30%), and the final exam (20%). Percentages denote portion of final grade attributed to each item. This distribution of points is subject to change.

Intent: This course provides a comprehensive introduction to the field of data mining. Topics covered include data preprocessing, predictive modeling, association analysis, clustering, classification, and anomaly detection.

Week(s) Topic Covered = completed
1 Introduction Covered
2-4 Data Covered
5-7 Classification Covered
8-10 Association Analysis Covered
11-13 Clustering Analysis Covered
14 Anomaly Detection Covered
15-16 Course Projects Covered