| Authors: | Michael Hahsler, Bettina GrĂ¼n, Kurt Hornik |
| Title: | [download] (4232)arules - A Computational Environment for Mining Association Rules and Frequent Item Sets |
| Reference: | Vol. 14, Issue 15, Sep 2005 Submitted 2005-04-15, Accepted 2005-09-29 |
| Type: | Article |
| 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. |
| Paper: | [download] (4232)arules - A Computational Environment for Mining Association Rules and Frequent Item Sets (application/pdf, 404.2 KB) |
| Supplements: | [download] (728)arules_0.2-5.tar.gz: R source package (application/x-gzip, 1.5 MB) |
| Resources: | BibTeX | OAI |
