A computational environment for mining association rules and frequent item sets

Michael Hahsler, Bettina Grün, Kurt Hornik

Publikation: Working/Discussion PaperWU Working Paper

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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)
OriginalspracheEnglisch
ErscheinungsortVienna
HerausgeberInstitut für Statistik und Mathematik, WU Vienna University of Economics and Business
DOIs
PublikationsstatusVeröffentlicht - 2005

Publikationsreihe

ReiheResearch Report Series / Department of Statistics and Mathematics
Nummer15

Bibliographische Notiz

Frühere Version

WU Working Paper Reihe

  • Research Report Series / Department of Statistics and Mathematics

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