Overview
Frequent item set mining
Christian Borgelt,
Corresponding Author
Christian Borgelt
European Centre for Soft Computing, Edificio de Investigación, Calle Gonzalo Gutiérrez Quirós s/n, Mieres, Asturias, Spain
European Centre for Soft Computing, Edificio de Investigación, Calle Gonzalo Gutiérrez Quirós s/n, Mieres, Asturias, Spain. WWW: http://www.borgelt.net/, http://www.softcomputing.es/Search for more papers by this authorChristian Borgelt,
Corresponding Author
Christian Borgelt
European Centre for Soft Computing, Edificio de Investigación, Calle Gonzalo Gutiérrez Quirós s/n, Mieres, Asturias, Spain
European Centre for Soft Computing, Edificio de Investigación, Calle Gonzalo Gutiérrez Quirós s/n, Mieres, Asturias, Spain. WWW: http://www.borgelt.net/, http://www.softcomputing.es/Search for more papers by this authorAbstract
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. © 2012 Wiley Periodicals, Inc.
This article is categorized under:
- Algorithmic Development > Association Rules
- Application Areas > Data Mining Software Tools
- Technologies > Association Rules
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