Top 10 algorithms in data mining

Xindong Wu; Vipin Kumar; Quinlan J. Ross; 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

doi:10.1007/s10115-007-0114-2

Top 10 algorithms in data mining

Xindong Wu, Vipin Kumar, Quinlan J. Ross, 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

Computer Science and Engineering

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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, k NN, 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.

Original language	English (US)
Pages (from-to)	1-37
Number of pages	37
Journal	Knowledge and Information Systems
Volume	14
Issue number	1
DOIs	https://doi.org/10.1007/s10115-007-0114-2
State	Published - Jan 2008

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AU - Wu, Xindong

AU - Kumar, Vipin

AU - Ross, Quinlan J.

AU - Ghosh, Joydeep

AU - Yang, Qiang

AU - Motoda, Hiroshi

AU - McLachlan, Geoffrey J.

AU - Ng, Angus

AU - Liu, Bing

AU - Yu, Philip S.

AU - Zhou, Zhi Hua

AU - Steinbach, Michael

AU - Hand, David J.

AU - Steinberg, Dan

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Top 10 algorithms in data mining

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