数据挖掘算法经典

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