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Visualizing association rules in hierarchical groups

Author

Listed:
  • Michael Hahsler

    (Southern Methodist University)

  • Radoslaw Karpienko

    (Vienna University of Economics and Business)

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.

Suggested Citation

  • Michael Hahsler & Radoslaw Karpienko, 2017. "Visualizing association rules in hierarchical groups," Journal of Business Economics, Springer, vol. 87(3), pages 317-335, April.
    • Handle: RePEc:spr:jbecon:v:87:y:2017:i:3:d:10.1007_s11573-016-0822-8
    DOI: 10.1007/s11573-016-0822-8
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    1. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.
    2. ., 2007. "The 1930s," Chapters, in: Henry W. de Jong & William G. Shepherd (ed.), Pioneers of Industrial Organization, chapter 11, Edward Elgar Publishing.
    3. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    4. ., 2007. "To the 1930s," Chapters, in: Henry W. de Jong & William G. Shepherd (ed.), Pioneers of Industrial Organization, chapter 10, Edward Elgar Publishing.
    5. Hahsler, Michael & Grün, Bettina & Hornik, Kurt, 2005. "arules - A Computational Environment for Mining Association Rules and Frequent Item Sets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i15).
    6. Martin Natter & Thomas Reutterer & Andreas Mild & Alfred Taudes, 2007. "—An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing," Marketing Science, INFORMS, vol. 26(4), pages 576-583, 07-08.
    7. Leeflang, Peter S.H. & Verhoef, Peter C. & Dahlström, Peter & Freundt, Tjark, 2014. "Challenges and solutions for marketing in a digital era," European Management Journal, Elsevier, vol. 32(1), pages 1-12.
    8. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    9. Robert P. Rooderkerk & Harald J. van Heerde & Tammo H. A. Bijmolt, 2013. "Optimizing Retail Assortments," Marketing Science, INFORMS, vol. 32(5), pages 699-715, September.
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    More about this item

    Keywords

    Association rules; Visualization; Shopping baskets; Exploratory analysis;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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