聚类分析
计算机科学
关联规则学习
朴素贝叶斯分类器
阿达布思
统计分类
数据挖掘
Apriori算法
支持向量机
算法
机器学习
数据流挖掘
人工智能
作者
Xindong Wu,Vipin Kumar,J. R. Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Shu‐Kay Ng,Bing Liu,Philip S. Yu,Zhi‐Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg
标识
DOI:10.1007/s10115-007-0114-2
摘要
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.
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