Top 10 algorithms in data mining

• Published in: Knowledge and information systems Vol. 14; no. 1; pp. 1 - 37
• Main Authors: Wu, Xindong, Kumar, Vipin, Ross Quinlan, J., Ghosh, Joydeep, Yang, Qiang, Motoda, Hiroshi, McLachlan, Geoffrey J., Ng, Angus, Liu, Bing, Yu, Philip S., Zhou, Zhi-Hua, Steinbach, Michael, Hand, David J., Steinberg, Dan
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.01.2008; Springer Nature B.V
• Subjects: Algorithms; Computer engineering; Computer Science; Data mining; Data Mining and Knowledge Discovery; Database Management; Decision trees; Information Storage and Retrieval; Information Systems and Communication Service; Information Systems Applications (incl.Internet); International conferences; IT in Business; Nominations; R&D; Ratings & rankings; Research & development; Survey Paper; Support Vector Machine; Association Rule; Apriori Algorithm; Frequent Itemsets; Mach Learn
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-007-0114-2

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.