数据挖掘领域十大经典算法

 

数据挖掘领域十大经典算法

 

下面是参与评比的18种算法，实际上随便拿出一种来都可以称得上是经典算法，它们在数据挖掘领域都产生了极为深远的影响。在我们学习数据挖掘时，可以以这18种算法为主线，如果能把每一种算法都弄懂，整个数据挖掘领域就掌握得差不多了。另外，也可以用这18种算法的熟悉程度来判断自己知识的掌握程度。

 

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