Wu, X.; Kumar, V.; Quinlan, J. R.; Ghosh, J.; Yang, Q.; Motoda, H.; McLachlan, G.; Ng, A.; Liu, B.; Yu, P.; Zhou, Z.-H.; Steinbach, M.; Hand, D. & Steinberg, D. (2008), 'Top 10 algorithms in data mining', Knowledge and Information Systems 14 (1) , 1--37 .
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This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community.With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current andfurther research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, associationanalysis, and link mining, which are all among the most important topics in data mining research and development.

Steinbach, M.; Ertoz, L. & Kumar, V. (2003), Challenges of Clustering High Dimensional Data, in L. T. Wille, ed., 'New Vistas in Statistical Physics -- Applications in Econophysics, Bioinformatics, and Pattern Recognition' , Springer-Verlag, .
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2002

Ertoz, L.; Steinbach, M. & Kumar, V. (2002), A New Shared Nearest Neighbor Clustering Algorithm and its Applications, in 'Workshop on Clustering High Dimensional Data and its Applications at 2nd SIAM International Conference on Data Mining' .
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