THE INSTRUCTOR

Dr. Chia-Hui Chang - Rm E6-B302, Ext: 35302
Homepage: http://www.csie.ncu.edu.tw/~chia/
Email: chia@csie.ncu.edu.tw

TEXTBOOK

• Data Mining:  Concepts and Techniques, J. Han and M. Kamber, Morgan Kaufmann , 2000
Slides can be downloaded from http://www.cs.sfu.ca/~han/DM_Book.html

REFERENCES

• Predictive Data Mining, S.M. Weiss and N. Indurkhya, Morgan Kaufmann, 1998
• Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation, Ian H. Witten and Eibe Frank, Morgan Kaufman, 1999
• Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusam, AAAI/MIT Press, 1996:  A recent collection of research papers
• Mastering Data Mining: The Art and Science of Customer Relationship Management, by Michael J. A. Berry and Gordon Linoff, John Wiley & Sons; 1999

SCHEDULE

• Week 2 to 8: Instructor Teaching
• Week 9 to 10: KDD Cup
• Week 11: Mid-term Exam
• Week 12 to 14: Paper reading
• Week 15 to 17: Project presentation

GRADING

• KDD Cup (20%)
• Mid-term (30%)
• Paper presentation (20%)
• Final project (30%)

COURSE CONTENT

1. Data Mining Techniques
• Data Warehouse and OLAP
• Data Description and Comparison
• Associations -- finding association rules, dependency/belief networks, market basket analysis
• Classification -- building a classification model  (Decision tree | Rules | Neural network | Bayesian | Other )
• Clustering - for finding clusters or segments
2. Data Mining Software -- Libraries and Developer Kits for creating embedded data mining applications
• DBMiner
• IBM Intelligent Miner for Data

USEFUL LINKS

• KDNuggets Directory: Data Mining and Knowledge Discovery

http://www.kdnuggets.com/index.html
• Data Mining Software

http://www.cs.bham.ac.uk/~anp/software.html