arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

Michael Hahsler, Bettina Grün and Kurt Hornik

Journal of Statistical Software, 2005, vol. 014, issue i15

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.

Date: 2005-09-29
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:014:i15

DOI: 10.18637/jss.v014.i15

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