Frequent item set mining

被引:217
作者
Borgelt, Christian [1 ]
机构
[1] European Ctr Soft Comp, Mieres, Asturias, Spain
关键词
ALGORITHM;
D O I
10.1002/widm.1074
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Frequent item set mining is one of the best known and most popular data mining methods. Originally developed for market basket analysis, it is used nowadays for almost any task that requires discovering regularities between (nominal) variables. This paper provides an overview of the foundations of frequent item set mining, starting from a definition of the basic notions and the core task. It continues by discussing how the search space is structured to avoid redundant search, how it is pruned with the a priori property, and how the output is reduced by confining it to closed or maximal item sets or generators. In addition, it reviews some of the most important algorithmic techniques and data structures that were developed to make the search for frequent item sets as efficient as possible. (c) 2012 Wiley Periodicals, Inc.
引用
收藏
页码:437 / 456
页数:20
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