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.

Keywords for this software

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References in zbMATH (referenced in 12 articles , 1 standard article )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Michael Scholz: R Package clickstream: Analyzing Clickstream Data with Markov Chains (2016)
  2. Ji, Zhanglong; Elkan, Charles: Differential privacy based on importance weighting (2013)
  3. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  4. Ledolter, Johannes: Data mining and business analytics with R (2013)
  5. Zhao, Yanchang: R and data mining. Examples and case studies (2013)
  6. Blockeel, Hendrik; Calders, Toon; Fromont, Élisa; Goethals, Bart; Prado, Adriana: An inductive database system based on virtual mining views (2012)
  7. Kenett, Ron S.: ’The COM-Poisson model for count data: a survey of methods and applications’ by K. Sellers, S. Borle and G. Shmueli (2012)
  8. Hahsler, Michael; Chelluboina, Sudheer; Hornik, Kurt; Buchta, Christian: The arules R-package ecosystem: analyzing interesting patterns from large transaction data sets (2011)
  9. Williams, Graham: Data Mining with Rattle and R. The art of excavating data for knowledge discovery. (2011)
  10. Hornik, Kurt; Buchta, Christian; Zeileis, Achim: Open-source machine learning: R meets Weka (2009)
  11. Hahsler, Michael; Buchta, Christian; Hornik, Kurt: Selective association rule generator (2008)
  12. Mcnicholas, P. D.; Murphy, T. B.; O’Regan, M.: Standardising the lift of an association rule (2008)