Building on the Arules Infrastructure for Analyzing Transaction Data with R

  • Michael Hahsler
  • Kurt Hornik
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


The free and extensible statistical computing environment R with its enormous number of extension packages already provides many state-of-the-art techniques for data analysis. Support for association rule mining, a popular exploratory method which can be used, among other purposes, for uncovering cross-selling opportunities in market baskets, has become available recently with the R extension package arules. After a brief introduction to transaction data and association rules, we present the formal framework implemented in arules and demonstrate how clustering and association rule mining can be applied together using a market basket data set from a typical retailer. This paper shows that implementing a basic infrastructure with formal classes in R provides an extensible basis which can very efficiently be employed for developing new applications (such as clustering transactions) in addition to association rule mining.


Association Rule Association Rule Mining Store Manager Dissimilarity Matrix Transaction Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michael Hahsler
    • 1
  • Kurt Hornik
    • 2
  1. 1.Department of Information Systems and OperationsWirtschaftsuniversitätWienAustria
  2. 2.Department of Statistics and MathematicsWirtschaftsuniversitätWienAustria

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