18 Candidates for the Top 10 Algorithms in Data Mining 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. Google Scholar Count in October 2006: 155