Visualizing association rules in hierarchical groups

Hahsler, Michael and Karpienko, Radoslaw (2016) Visualizing association rules in hierarchical groups. Journal of Business Economics, 87 (3). pp. 317-335. ISSN 0044-2372

Available under License Creative Commons: Attribution 4.0 International (CC BY 4.0).

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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. (authors' abstract)

Item Type: Article
Additional Information: This article is published with open access at Open access funding provided by Vienna University of Economics and Business (WU).
Keywords: association rules / visualization / shopping baskets / exploratory analysis
Classification Codes: JEL M3, C6, C8
Divisions: Departments > Marketing > Service Marketing und Tourismus
Version of the Document: Published
Variance from Published Version: None
Depositing User: ePub Administrator
Date Deposited: 11 May 2016 07:33
Last Modified: 06 Apr 2017 16:45


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