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A computational environment for mining association rules and frequent item sets

By Michael Hahsler, Bettina Grün and Kurt Hornik

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)Series: Research Report Series / Department of Statistics and Mathematic

Topics: Data Mining / Association Rules / Frequent Itemsets / Implementation
Publisher: Institut für Statistik und Mathematik, WU Vienna University of Economics and Business
Year: 2005
OAI identifier: oai:epub.wu-wien.ac.at:epub-wu-01_821

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