Top 10 algorithms in data mining

by: Xindong Wu, Vipin Kumar, Ross, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey Mclachlan, Angus Ng, Bing Liu, Philip Yu, Zhi-Hua Zhou, Michael Steinbach, David Hand, Dan Steinberg

Knowledge and Information Systems, Vol. 14, No. 1. (20 January 2008), pp. 1-37.

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Abstract

Abstract 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 and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

@article{citeulike:2320499, abstract = {Abstract\ \ 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 and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.}, author = {Wu, Xindong and Kumar, Vipin and Ross and Ghosh, Joydeep and Yang, Qiang and Motoda, Hiroshi and Mclachlan, Geoffrey and Ng, Angus and Liu, Bing and Yu, Philip and Zhou, Zhi-Hua and Steinbach, Michael and Hand, David and Steinberg, Dan }, citeulike-article-id = {2320499}, doi = {10.1007/s10115-007-0114-2}, journal = {Knowledge and Information Systems}, keywords = {data\_mining}, month = {January}, number = {1}, pages = {1--37}, posted-at = {2008-03-07 17:26:45}, priority = {4}, title = {Top 10 algorithms in data mining}, url = {http://portal.acm.org/citation.cfm?id=1327434.1327436}, volume = {14}, year = {2008} }

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TY - JOUR ID - citeulike:2320499 L3 - citeulike-article-id:2320499 TI - Top 10 algorithms in data mining JF - Knowledge and Information Systems VL - 14 IS - 1 SP - 1 EP - 37 N2 - Abstract  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 and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development. KW - data_mining AU - Wu, Xindong AU - Kumar, Vipin AU - Ross AU - Ghosh, Joydeep AU - Yang, Qiang AU - Motoda, Hiroshi AU - Mclachlan, Geoffrey AU - Ng, Angus AU - Liu, Bing AU - Yu, Philip AU - Zhou, Zhi-Hua AU - Steinbach, Michael AU - Hand, David AU - Steinberg, Dan PY - 2008/01/20/ UR - http://portal.acm.org/citation.cfm?id=1327434.1327436 ER -

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