Top 10 algorithms in data mining

by Xindong Wu, Vipin Kumar, John Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey 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 ()
Get full text at journal

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.

Cite this document (BETA)

Readership Statistics

1 Reader on Mendeley
by Discipline
 
by Academic Status
 
100% Ph.D. Student

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Sign up & Download

Already have an account? Sign in