Top 10 algorithms in data mining

DMCA

Top 10 algorithms in data mining (2007)

Cached

Download Links

by Xindong Wu , Vipin Kumar , J. Ross , Quinlan Joydeep , Ghosh Qiang Yang , Hiroshi Motoda , Geoffrey J. Mclachlan , Angus Ng , Bing Liu , Philip S. Yu , Dan Steinberg , X. Wu (b , V. Kumar , J. Ross Quinlan , J. Ghosh , Q. Yang , H. Motoda

Citations:

126 - 2 self

BibTeX

@MISC{Wu07top10,
    author = {Xindong Wu and Vipin Kumar and J. Ross and Quinlan Joydeep and Ghosh Qiang Yang and Hiroshi Motoda and Geoffrey J. Mclachlan and Angus Ng and Bing Liu and Philip S. Yu and Dan Steinberg and X. Wu (b and V. Kumar and J. Ross Quinlan and J. Ghosh and Q. Yang and H. Motoda},
    title = {Top 10 algorithms in data mining},
    year = {2007}
}

OpenURL

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,

Keyphrases

data mining influential data mining algorithm data mining algorithm research community ieee international conference naive bayes algorithm cover classification

DMCA