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.
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#2. CART
L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and
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#3. K Nearest Neighbours (kNN)
Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest
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#4. Naive Bayes
Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All?
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Statistical Learning
====================
#5. SVM
Vapnik, V. N. 1995. The Nature of Statistical Learning
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#6. EM
McLachlan, G. and Peel, D. (2000). Finite Mixture Models.
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Google Scholar Count in October 2006: 848
Association Analysis
====================
#7. Apriori
Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining
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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
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Google Scholar Count in October 2006: 1258
Link Mining
===========
#9. PageRank
Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual
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Google Shcolar Count: 2558
#10. HITS
Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked
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Google Shcolar Count: 2240
Clustering
==========
#11. K-Means
MacQueen, J. B., Some methods for classification and analysis of
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#12. BIRCH
Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient
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Bagging and Boosting
====================
#13. AdaBoost
Freund, Y. and Schapire, R. E. 1997. A decision-theoretic
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Sequential Patterns
===================
#14. GSP

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