Prerequisite: CS 688 or permission of instructor
001
14007 R
7: 20 pm – 10:00 pm
IN 136
Office Hours: R 6:15 pm – 7:15 pm (ST II – rm. 461)
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:
á databases,
data warehousing, data
mining, knowledge discovery, and the Semantic Web http://www.w3.org/2001/sw
á data exploration
á data reduction and transformation
á classification
á association
á clustering
á anomaly detection
á applications: web mining
á
Homework Assignments 15%
á
MidTerm 25%
á
Term TEAM Project 30%
á
Final (Wed. 5/7) 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