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Top 10 algorithms in data mining

by Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg show all authors
Knowledge and Information Systems ()

Abstract

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) inDecember 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among themost influential datamining algorithms in the research community.With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and reviewcurrent and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and linkmining, which are all among the most important topics in data mining research and development.

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