Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
arules - A Computational Environment for Mining Association Rules and Frequent Item Sets | Hahsler | Journal of Statistical Software
Authors: Michael Hahsler, Bettina Grün, Kurt Hornik
Title: arules - A Computational Environment for Mining Association Rules and Frequent Item Sets
Abstract: Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.

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Paper: arules - A Computational Environment for Mining Association Rules and Frequent Item Sets     Download PDF (Downloads: 18380)
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DOI: 10.18637/jss.v014.i15

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