Visualizing association rules in hierarchical groups

Michael Hahsler, Radoslaw Karpienko

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung

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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.
OriginalspracheEnglisch
Seiten (von - bis)1 - 19
FachzeitschriftJournal of Business Economics (JBE) (früher: Zeitschrift für Betriebswirtschaft ZfB)
DOIs
PublikationsstatusVeröffentlicht - 2016

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