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高级数据挖掘:原理、算法与应用》课程相关信息

http://www.iipl.fudan.edu.cn/ds08/acco.htm

高级数据挖掘:原理、算法与应用课程简介

http://www.iipl.fudan.edu.cn/ds08/index.htm

ADVANCED DATA MINING: PRINCIPLES, ALGORITHMS, and APPLICATIONS

 

Jiawei Han  Professor

Department of Computer Science

University of Illinois at Urbana-Champaign

 

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).

 

MAJOR TOPICS:

  1. General overview of data mining
  2. Advanced data integration and preprocessing
  3. Data warehouse, data cube, OLAP and multidimensional analysis
  4. Frequent pattern and correlation analysis
  5. Classification and predictive modeling
  6. Cluster analysis
  7. Mining sequence and time-series data
  8. Mining graphs and structured patterns
  9. Link analysis and mining information networks
  10. Stream data mining
  11. Mining spatial, spatiotemporal, RFID data, trajectories, and moving objects
  12. Mining multimedia, text, and web data
  13. Data mining applications: Software engineering and bioinformatics
  14. Other issues on data mining: visual data mining and privacy-preserving data mining
  15. Research frontiers and social impacts of data mining

 

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.