Prerequisite:
CS 688 or permission of instructor
A01 41083 /
41088 5/18 MWF
7: 00 pm – 10:05 pm SITE I 206
Office
Hours: TBD (ENGR - 4448)
Catalog description: Concepts
and techniques in data mining and multidisciplinary applications. Topics
include databases; data cleaning and transformation; concept description;
association and correlation rules; data classification and predictive modeling;
performance analysis and scalability, data mining in advanced database systems,
including text, audio, and images; and emerging themes and trends. Term team
project and topical review are required.
Instructor:
Prof. Harry Wechsler http://cs.gmu.edu/~wechsler/
Textbook:
Tan, Steinbach and
Kumar, Introduction to Data Mining,
Pearson / Addison Wesley, 2006
textbook slides: http://www-users.cs.umn.edu/~kumar/dmbook/
References
WEKA
web site for data mining software
http://www.togaware.com/datamining/survivor/Weka.html
UCI
Machine Learning Repository Content Summary
http://www.ics.uci.edu/~mlearn/MLSummary.html
Syllabus:
·
databases, data
warehousing, data mining, knowledge discovery, and the Semantic Web
(W3C) http://www.w3.org/2001/sw
·
prediction, model selection, validation, and
performance evaluation
·
data exploration
·
data reduction
and transformation
·
classification
·
association
·
clustering
·
anomaly
detection
·
applications: biometrics
·
Homework Assignments
– 10%
·
Quizzes (5/26
& 6/5) – 15%
·
Mid Term – June 9 – 20%
·
Term TEAM
Project – 25%
·
Final – June 19 – 30%
You are
expected to abide by the honor code. Homework assignments and exams are
individual efforts. Information on the university honor code can be found at: http://jiju.gmu.edu/catalog/apolicies/honor.html