Hahsler, Michael and Grün, Bettina and Hornik, Kurt
A computational environment for mining association rules and frequent item sets.
Research Report Series / Department of Statistics and Mathematics, 15.
Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, Vienna.
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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