ADVANCED DATA MINING: PRINCIPLES, ALGORITHMS, and APPLICATIONS
Jiawei Han Professor
Department of Computer Science
SYNOPSIS: The course introduces the principles, algorithms, and applications of advanced data mining, including algorithms, methods, implementations and applications of classification, clustering, association and correlation analysis, multidimensional and OLAP analysis, mining sequential and structured data, stream data, text data, Web data, spatiotemporal data, biomedical data and other forms of complex data.
PREREQUISITES: An introductory course on database systems, statistics, machine learning, or data mining at undergraduate or graduate level (or equivalent).
TEXTBOOK and MAJOR REFERENCES:
1. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann, 2006
2. Recent research papers in major conference proceedings, including ACM SIGKDD (KDD), ACM SIGMOD, VLDB, ICDM, SDM (SIAM Data Mining conference), ICDE, ICML, WWW, and other related conferences.
OTHER REFERENCE BOOKS:
1. S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertext and Semi-Structured Data”, Morgan Kaufmann, 2002.
2. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001.
3. B. Liu. Web Data Mining, Springer 2006.
4. T. M. Mitchell. Machine Learning, McGraw Hill, 1997.
5. P.-N.Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining, Addison-Wesley, 2006.
6. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed., 2005.