top 10 algorithms in data mining

    Data mining is abroad area that integrates techniques from several fields including machine learning,statistics,patternrecognition,artificial intelligence,and database systems,for the analysis of large volumes of data. 

    the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM)in December 2006:C4.5,k-Means,SVM, Apriori,EM,PageRank,AdaBoost,kNN,NaiveBayes,andCART. These top 10 algorithms are among the most influential data mining algorithms in the research community.

#1: C4.5 (61 votes), 
#2: K-Means (60 votes),
#3: SVM (58 votes), 
#4: Apriori (52 votes), 
#5: EM (48 votes), 
#6: PageRank (46 votes), 
#7: AdaBoost (45 votes), 
#7: kNN (45 votes), 
#7: Naive Bayes (45 votes), 
#10: CART (34 votes),

Classification

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

 

 #1. C4.5

 

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

Morgan Kaufmann Publishers Inc.

 

 

 #2. CART

 

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

Regression Trees. Wadsworth , Belmont , CA , 1984.

 

 #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

 

 #4. Naive Bayes

 

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

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

 

 

Statistical Learning

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

 

 #5. SVM

 

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

Theory. Springer-Verlag New York, Inc.

 

 #6. EM

 

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

J. Wiley, New York .

 

 

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

 

 #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

 

 

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

 

 #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.

 

 

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.

 

 #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

 

 

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

 

 

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.

 

 #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 .

 

 

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

               

 

Rough Sets

==========

 

 #17. Finding reduct

 

Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about

Data, Kluwer Academic Publishers, Norwell , MA , 1992

 

 

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 .