Visualizing association rules in hierarchical groups

Michael Hahsler and Radoslaw Karpienko ()
Additional contact information
Michael Hahsler: Southern Methodist University
Radoslaw Karpienko: Vienna University of Economics and Business

Journal of Business Economics, 2017, vol. 87, issue 3, No 4, 317-335

Abstract: 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.

Keywords: Association rules; Visualization; Shopping baskets; Exploratory analysis (search for similar items in EconPapers)
JEL-codes: C6 C8 M3 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11573-016-0822-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jbecon:v:87:y:2017:i:3:d:10.1007_s11573-016-0822-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11573

DOI: 10.1007/s11573-016-0822-8

Access Statistics for this article

Journal of Business Economics is currently edited by Günter Fandel

More articles in Journal of Business Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().