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  • 标题:arules - A Computational Environment for Mining Association Rules and Frequent Item Sets
  • 本地全文:下载
  • 作者:Michael Hahsler ; Bettina Grün ; Kurt Hornik
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2005
  • 卷号:14
  • 期号:1
  • 页码:1-25
  • 语种:English
  • 出版社:University of California, Los Angeles
  • 摘要: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.
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