arules

arules: Mining Association Rules and Frequent Itemsets Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides interfaces to C implementations of the association mining algorithms Apriori and Eclat by C. Borgelt.

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References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Ji, Zhanglong; Elkan, Charles: Differential privacy based on importance weighting (2013)
  2. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  3. Ledolter, Johannes: Data mining and business analytics with R (2013)
  4. Zhao, Yanchang: R and data mining. Examples and case studies (2013)
  5. Blockeel, Hendrik; Calders, Toon; Fromont, Élisa; Goethals, Bart; Prado, Adriana: An inductive database system based on virtual mining views (2012)
  6. Hahsler, Michael; Chelluboina, Sudheer; Hornik, Kurt; Buchta, Christian: The arules R-package ecosystem: analyzing interesting patterns from large transaction data sets (2011)
  7. Williams, Graham: Data Mining with Rattle and R. The art of excavating data for knowledge discovery. (2011)
  8. Hornik, Kurt; Buchta, Christian; Zeileis, Achim: Open-source machine learning: R meets Weka (2009)
  9. Hahsler, Michael; Buchta, Christian; Hornik, Kurt: Selective association rule generator (2008)
  10. Mcnicholas, P.D.; Murphy, T.B.; O’Regan, M.: Standardising the lift of an association rule (2008)