A computational environment for mining association rules and frequent item sets

Michael Hahsler; Bettina Grün; Kurt Hornik

doi:10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822

A computational environment for mining association rules and frequent item sets

Michael Hahsler, Bettina Grün, Kurt Hornik

Institute for Statistics and Mathematics

Publication: Working/Discussion Paper › WU Working Paper

123 Downloads (Pure)

Abstract

Mining frequent itemsets and association rules is a popular and well researched approach to 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. (author's abstract)

Original language	English
Place of Publication	Vienna
Publisher	Institut für Statistik und Mathematik, WU Vienna University of Economics and Business
DOIs	https://doi.org/10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822
Publication status	Published - 2005

Publication series

Series	Research Report Series / Department of Statistics and Mathematics
Number	15

Bibliographical note

Earlier version

WU Working Paper Series

Research Report Series / Department of Statistics and Mathematics

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10.57938/384acceb-9bcf-4cfe-b0c7-f745184a0822Licence: Unspecified

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1 Journal article

arules - A computational environment for mining association rules and frequent item sets
Hahsler, M., Grün, B. & Hornik, K., 2005, In: Journal of Statistical Software. 14, 15, p. 1 - 25
Publication: Scientific journal › Journal article › peer-review

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AB - Mining frequent itemsets and association rules is a popular and well researched approach to 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. (author's abstract)

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A computational environment for mining association rules and frequent item sets

Abstract

Publication series

Bibliographical note

WU Working Paper Series

Access to Document

Other versions

arules - A computational environment for mining association rules and frequent item sets

Cite this