Introduction to Data Mining
COMP 337, Fall 2010
Mon/Wed 09:00-10:20 Room 2053
Tutorials on Wed 17:00-17:50, Room 2406
Qiang Yang (qyang@cse.
); Office: Room 3563,
2358-8768. Office Hours: Tuesdays 1:30-3:00pm
Teaching Assistant: Sophie Si Shen (shensi@cse.)
Office hours: Fridays 10:30-13:00.
Assignment Hand-in: email to TA.
This course will
provide an introduction to concepts and techniques in the field of data mining.
Material include an introduction to data preprocessing and the techniques used
to explore the large quantities of data for the discovery of predictive models
and knowledge. The course will include data mining techniques such as
nearest neighbor, decision trees, neural networks, Bayesian networks and Na´ve
Bayes, rule-based methods, association analysis and clustering, as well as
social networks and data mining applications in business and finance
applications. We will also cover emerging data mining areas and
applications. Students learn the material by attending lectures and implementing
and applying different data analysis and data mining techniques to large
- Introduction to Data Mining (1
- Data Warehouse (1 Week)
- Classification (2-3 Weeks)
- Clustering (2-3 Weeks)
- Association Rules (1-2 Weeks)
- Web Data Mining (1-2 Weeks)
- Social Network Analysis (1-2 Weeks)
The course work includes assignments and projects, a
midterm exam and a final exam. The marking scheme is as follows.
- 4 Assignments -- 20%
- 2 Course Projects -- 20 %
- 2 Exams -- 60%
- Introduction to Data Mining
by Pang-Ning Tan, Michael Steinbach, and
Vipin Kumar, Pearson International Edition, 2005 .
- Data Mining: Concepts and
Techniques, 2nd edition by Jiawei Han and
Micheline Kamber. Morgan Kaufmann Publishers, 2006.
Qiang Yang's Homepage