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  • 数据挖掘领域最有影响力的18个算法(转载)

    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.

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  • 原文地址:https://www.cnblogs.com/zqliu133/p/4966568.html
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