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首頁(yè)精彩閱讀數(shù)據(jù)挖掘領(lǐng)域十大經(jīng)典算法
數(shù)據(jù)挖掘領(lǐng)域十大經(jīng)典算法
2014-11-02
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     數(shù)據(jù)挖掘領(lǐng)域十大經(jīng)典算法

下面是參與評(píng)比的18種算法，實(shí)際上隨便拿出一種來(lái)都可以稱(chēng)得上是經(jīng)典算法，它們?cè)?a target="_blank" href="http://www.3lll3.cn/kecheng/4.html">數(shù)據(jù)挖掘領(lǐng)域都產(chǎn)生了極為深遠(yuǎn)的影響。在我們學(xué)習(xí)數(shù)據(jù)挖掘時(shí)，可以以這18種算法為主線，如果能把每一種算法都弄懂，整個(gè)數(shù)據(jù)挖掘領(lǐng)域就掌握得差不多了。另外，也可以用這18種算法的熟悉程度來(lái)判斷自己知識(shí)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
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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

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