http://www.bibsonomy.org/burst/user/jaeschke/icdm+miningBibSonomy BuRST Feed for /user/jaeschke/icdm+mining2008-08-21T11:57:39+02:00http://www.bibsonomy.org/bibtex/22c34bb4b49187a6d3e780e78d254ae1f/jaeschkejaeschke2008-01-18T11:04:39+01:00ieee mining top data algorithm icdm <span style="color:#555555;">Xindong <a href="http://www.bibsonomy.org/author/Wu">Wu</a> and Vipin <a href="http://www.bibsonomy.org/author/Kumar">Kumar</a> and J. <a href="http://www.bibsonomy.org/author/Ross Quinlan">Ross Quinlan</a> and Joydeep <a href="http://www.bibsonomy.org/author/Ghosh">Ghosh</a> and Qiang <a href="http://www.bibsonomy.org/author/Yang">Yang</a> and Hiroshi <a href="http://www.bibsonomy.org/author/Motoda">Motoda</a> and Geoffrey <a href="http://www.bibsonomy.org/author/McLachlan">McLachlan</a> and Angus <a href="http://www.bibsonomy.org/author/Ng">Ng</a> and Bing <a href="http://www.bibsonomy.org/author/Liu">Liu</a> and Philip <a href="http://www.bibsonomy.org/author/Yu">Yu</a> and Zhi-Hua <a href="http://www.bibsonomy.org/author/Zhou">Zhou</a> and Michael <a href="http://www.bibsonomy.org/author/Steinbach">Steinbach</a> and David <a href="http://www.bibsonomy.org/author/Hand">Hand</a> and Dan <a href="http://www.bibsonomy.org/author/Steinberg">Steinberg</a> </span><em>Knowledge and Information Systems</em><em>14(1):1--37</em><em>Jan2008. </em>Fri Jan 18 11:04:39 CET 2008LondonKnowledge and Information SystemsJan11--37Top 10 algorithms in data mining142008ieee mining top data algorithm icdm 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 andfurther research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, associationanalysis, and link mining, which are all among the most important topics in data mining research and development.