【数据挖掘】数据挖掘领域最有影响力的18个算法

ICDM2006-介绍：数据挖掘领域最有影响力的18个算法

ICDM是数据挖掘领域的顶级会议之一，在数据挖掘理论与应用领域具有相当影响力。 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. 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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.