Abstract
Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Although visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming, most association rule visualization techniques are still falling short when it comes to large numbers of rules. In this paper we introduce a new interactive visualization method, the grouped matrix representation, which allows to intuitively explore and interpret highly complex scenarios. We demonstrate how the method can be used to analyze large sets of association rules using the R software for statistical computing, and provide examples from the implementation in the R-package arulesViz.
Highlights
Businesses nowadays collect and store unprecedented amounts of customer data on a daily basis, and the so-called ‘data explosion’ has been identified as one of the major challenges for marketers in both, online and offline channels
Mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones
Visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming, most association rule visualization techniques are still falling short when it comes to large numbers of rules
Summary
Businesses nowadays collect and store unprecedented amounts of customer data on a daily basis, and the so-called ‘data explosion’ has been identified as one of the major challenges for marketers in both, online and offline channels A number of publications have presented data visualization techniques as a means to extract meaningful results from highly complex settings (Lee and Bradlow 2011; Netzer et al 2012). Several approaches have been proposed to model shopping baskets (i.e. interrelations between categories), which can be generally summarized into explanatory or exploratory techniques (Mild and Reutterer 2003; Boztugand Silberhorn 2006). Marketing publications have proposed the use of social network graphs to uncover interrelations between products and brands (Netzer et al 2012; Lee and Bradlow 2011) These studies have shown that network analysis techniques were capable of dealing with large data, and underlined the value of visualizing extracted patterns in a comprehensible way.
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