Quotation Hahsler, Michael, Karpienko, Radoslaw. 2016. Visualizing association rules in hierarchical groups. Journal of Business Economics (JBE) (früher: Zeitschrift für Betriebswirtschaft ZfB) , 1-19.




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.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Business Economics (JBE) (früher: Zeitschrift für Betriebswirtschaft ZfB)
WU Journalrating 2009 A
WU-Journal-Rating new FIN-A, INF-A, STRAT-B, WH-B
Language English
Title Visualizing association rules in hierarchical groups
Year 2016
Page from 1
Page to 19
Reviewed? Y
URL http://link.springer.com/article/10.1007/s11573-016-0822-8
DOI http://dx.doi.org/ 10.1007/s11573-016-0822-8
JEL M3 C6 C8


Karpienko, Radoslaw (Former researcher)
Hahsler, Michael (SMU Lyle School of Engineering, United States/USA)
Service Marketing and Tourism IN (Details)
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