Building on the Arules Infrastructure for Analyzing Transaction Data with R View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2007

AUTHORS

Michael Hahsler , Kurt Hornik

ABSTRACT

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. More... »

PAGES

449-456

References to SciGraph publications

  • 1997-07. Perspectives on Multiple Category Choice in MARKETING LETTERS
  • Book

    TITLE

    Advances in Data Analysis

    ISBN

    978-3-540-70980-0
    978-3-540-70981-7

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-70981-7_51

    DOI

    http://dx.doi.org/10.1007/978-3-540-70981-7_51

    DIMENSIONS

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