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.
CITATION STYLE
Hahsler, M., & Hornik, K. (2007). Building on the arules infrastructure for analyzing transaction data with R. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 449–456). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-540-70981-7_51
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