18 Candidates for the Top 10 Algorithms in Data MiningClassification============== #1. C4.5Quinlan, J. R. 1993. C4.5: Programs for Machine Learning.Morgan Kaufmann Publishers Inc. Google Scholar Count in October 2006: 6907 #2. CARTL. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification andRegression 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 NearestNeighbor Classification. IEEE Trans. PatternAnal. Mach. Intell. (TPAMI). 18, 6 (Jun. 1996), 607-616. DOI= http://dx.doi.org/10.1109/34.506411Google SCholar Count: 183 #4. Naive BayesHand, 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: 51Statistical Learning==================== #5. SVMVapnik, V. N. 1995. The Nature of Statistical LearningTheory. Springer-Verlag New York, Inc. Google Scholar Count in October 2006: 6441 #6. EMMcLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York.Google Scholar Count in October 2006: 848Association Analysis==================== #7. AprioriRakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for MiningAssociation Rules. In Proc. of the 20th Int'l Conference on Very LargeDatabases (VLDB '94), Santiago, Chile, September 1994. http://citeseer.comp.nus.edu.sg/agrawal94fast.htmlGoogle Scholar Count in October 2006: 3639 #8. FP-TreeHan, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns withoutcandidate generation. In Proceedings of the 2000 ACM SIGMODinternational Conference on Management of Data (Dallas, Texas, UnitedStates, May 15 - 18, 2000). SIGMOD '00. ACM Press, New York, NY, 1-12.DOI= http://doi.acm.org/10.1145/342009.335372Google Scholar Count in October 2006: 1258Link Mining=========== #9. PageRankBrin, S. and Page, L. 1998. The anatomy of a large-scale hypertextualWeb search engine. In Proceedings of the Seventh internationalConference on World Wide Web (WWW-7) (Brisbane,Australia). P. H. Enslow and A. Ellis, Eds. Elsevier SciencePublishers B. V., Amsterdam, The Netherlands, 107-117. DOI= http://dx.doi.org/10.1016/S0169-7552(98)00110-XGoogle Shcolar Count: 2558 #10. HITSKleinberg, J. M. 1998. Authoritative sources in a hyperlinkedenvironment. In Proceedings of the Ninth Annual ACM-SIAM Symposium onDiscrete Algorithms (San Francisco, California, United States, January25 - 27, 1998). Symposium on Discrete Algorithms. Society forIndustrial and Applied Mathematics, Philadelphia, PA, 668-677.Google Shcolar Count: 2240Clustering========== #11. K-MeansMacQueen, J. B., Some methods for classification and analysis ofmultivariate observations, in Proc. 5th Berkeley Symp. MathematicalStatistics and Probability, 1967, pp. 281-297.Google Scholar Count in October 2006: 1579 #12. BIRCHZhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficientdata clustering method for very large databases. In Proceedings of the1996 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/10.1145/233269.233324Google Scholar Count in October 2006: 853Bagging and Boosting==================== #13. AdaBoostFreund, Y. and Schapire, R. E. 1997. A decision-theoreticgeneralization of on-line learning and an application toboosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139. DOI= http://dx.doi.org/10.1006/jcss.1997.1504Google Scholar Count in October 2006: 1576Sequential Patterns=================== #14. GSPSrikant, R. and Agrawal, R. 1996. Mining Sequential Patterns:Generalizations and Performance Improvements. In Proceedings of the5th 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 ComputerScience, vol. 1057. Springer-Verlag, London, 3-17.Google Scholar Count in October 2006: 596 #15. PrefixSpanJ. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal andM-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently byPrefix-Projected Pattern Growth. In Proceedings of the 17thinternational Conference on Data Engineering (April 02 - 06,2001). ICDE '01. IEEE Computer Society, Washington, DC. Google Scholar Count in October 2006: 248Integrated Mining================= #16. CBALiu, B., Hsu, W. and Ma, Y. M. Integrating classification andassociation rule mining. KDD-98, 1998, pp. 80-86. http://citeseer.comp.nus.edu.sg/liu98integrating.htmlGoogle Scholar Count in October 2006: 436 Rough Sets========== #17. Finding reductZdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning aboutData, Kluwer Academic Publishers, Norwell, MA, 1992Google Scholar Count in October 2006: 329Graph Mining============ #18. gSpanYan, X. and Han, J. 2002. gSpan: Graph-Based Substructure PatternMining. In Proceedings of the 2002 IEEE International Conference onData Mining (ICDM '02) (December 09 - 12, 2002). IEEE ComputerSociety, Washington, DC.Google Scholar Count in October 2006: 155
