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

Hornik, Kurt and Grün, Bettina and Hahsler, Michael (2005) arules - A Computational Environment for Mining Association Rules and Frequent Item Sets. Journal of Statistical Software, 14 (15). pp. 1-25. ISSN 1548-7660

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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. (authors' abstract)

Item Type: Article
Keywords: data mining / association rules / frequent itemsets / R
Version of the Document: Published
Depositing User: ePub Administrator
Date Deposited: 26 Sep 2013 14:25
Last Modified: 24 Feb 2017 13:49
FIDES Link: https://bach.wu.ac.at/d/research/results/35951/
URI: http://epub.wu.ac.at/id/eprint/3976

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