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A computational environment for mining association rules and frequent item sets

Hahsler, Michael and Grün, Bettina and Hornik, Kurt (2005) 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.

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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)

Item Type: Paper
Keywords: Data Mining / Association Rules / Frequent Itemsets / Implementation
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 15 Apr 2005 16:50
Last Modified: 27 Sep 2013 06:52
URI: http://epub.wu.ac.at/id/eprint/132

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