重庆市田家炳中学图片:数据挖掘10大算法(from ICDM 2006)

Classification

==============

 #1. C4.5

Quinlan, J. R. 1993. C4.5: Programs for Machine Learning.

Morgan Kaufmann Publishers Inc.

Google Scholar Count in October 2006: 6907

 #2. CART

L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and

Regression Trees. Wadsworth, Belmont, CA, 1984.

Google Scholar Count in October 2006: 6078

 #3. K Nearest Neighbours (kNN)

Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest

Neighbor Classification. IEEE Trans. Pattern

Anal. Mach. Intell. (TPAMI). 18, 6 (Jun. 1996), 607-616. 

DOI= http://dx.doi.org/10.1109/34.506411

Google SCholar Count: 183

 #4. Naive Bayes

Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All?

Internat. Statist. Rev. 69, 385-398.

Google Scholar Count in October 2006: 51

Statistical Learning

====================

 #5. SVM

Vapnik, V. N. 1995. The Nature of Statistical Learning

Theory. Springer-Verlag New York, Inc.

Google Scholar Count in October 2006: 6441

 #6. EM

McLachlan, G. and Peel, D. (2000). Finite Mixture Models. 

J. Wiley, New York.

Google Scholar Count in October 2006: 848

Association Analysis

====================

 #7. Apriori

Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining

Association Rules. In Proc. of the 20th Int'l Conference on Very Large

Databases (VLDB '94), Santiago, Chile, September 1994. 

http://citeseer.comp.nus.edu.sg/agrawal94fast.html

Google Scholar Count in October 2006: 3639

 #8. FP-Tree

Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without

candidate generation. In Proceedings of the 2000 ACM SIGMOD

international Conference on Management of Data (Dallas, Texas, United

States, May 15 - 18, 2000). SIGMOD '00. ACM Press, New York, NY, 1-12.

DOI= http://doi.acm.org/10.1145/342009.335372

Google Scholar Count in October 2006: 1258

Link Mining

===========

 #9. PageRank

Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual

Web search engine. In Proceedings of the Seventh international

Conference on World Wide Web (WWW-7) (Brisbane,

Australia). P. H. Enslow and A. Ellis, Eds. Elsevier Science

Publishers B. V., Amsterdam, The Netherlands, 107-117. 

DOI= http://dx.doi.org/10.1016/S0169-7552(98)00110-X

Google Shcolar Count: 2558

 #10. HITS

Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked

environment. In Proceedings of the Ninth Annual ACM-SIAM Symposium on

Discrete Algorithms (San Francisco, California, United States, January

25 - 27, 1998). Symposium on Discrete Algorithms. Society for

Industrial and Applied Mathematics, Philadelphia, PA, 668-677.

Google Shcolar Count: 2240

Clustering

==========

 #11. K-Means

MacQueen, J. B., Some methods for classification and analysis of

multivariate observations, in Proc. 5th Berkeley Symp. Mathematical

Statistics and Probability, 1967, pp. 281-297.

Google Scholar Count in October 2006: 1579

 #12. BIRCH

Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient

data clustering method for very large databases. In Proceedings of the

1996 ACM SIGMOD international Conference on Management of Data

(Montreal, Quebec, Canada, June 04 - 06, 1996). J. Widom, Ed. 

SIGMOD '96. ACM Press, New York, NY, 103-114. 

DOI= http://doi.acm.org/10.1145/233269.233324

Google Scholar Count in October 2006: 853

Bagging and Boosting

====================

 #13. AdaBoost

Freund, Y. and Schapire, R. E. 1997. A decision-theoretic

generalization of on-line learning and an application to

boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139. 

DOI= http://dx.doi.org/10.1006/jcss.1997.1504

Google Scholar Count in October 2006: 1576

Sequential Patterns

===================

 #14. GSP

Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns:

Generalizations and Performance Improvements. In Proceedings of the

5th international Conference on Extending Database Technology:

Advances in Database Technology (March 25 - 29, 1996). P. M. Apers,

M. Bouzeghoub, and G. Gardarin, Eds. Lecture Notes In Computer

Science, vol. 1057. Springer-Verlag, London, 3-17.

Google Scholar Count in October 2006: 596

 #15. PrefixSpan

J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and

M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by

Prefix-Projected Pattern Growth. In Proceedings of the 17th

international Conference on Data Engineering (April 02 - 06,

2001). ICDE '01. IEEE Computer Society, Washington, DC.

Google Scholar Count in October 2006: 248

Integrated Mining

=================

 #16. CBA

Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and

association rule mining. KDD-98, 1998, pp. 80-86. 

http://citeseer.comp.nus.edu.sg/liu98integrating.html

Google Scholar Count in October 2006: 436

Rough Sets

==========

 #17. Finding reduct

Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about

Data, Kluwer Academic Publishers, Norwell, MA, 1992

Google Scholar Count in October 2006: 329

Graph Mining

============

 #18. gSpan

Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern

Mining. In Proceedings of the 2002 IEEE International Conference on

Data Mining (ICDM '02) (December 09 - 12, 2002). IEEE Computer

Society, Washington, DC.

Google Scholar Count in October 2006: 155