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数据挖掘领域最有影响力的18个算法(转载)
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发布时间:2019-06-13

本文共 5661 字,大约阅读时间需要 18 分钟。

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.

转载于:https://www.cnblogs.com/zqliu133/p/4966568.html

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