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Visual Comparison of Association Rules

Author

Listed:
  • Heike Hofmann

    (Augsburg University)

  • Adalbert Wilhelm

    (Augsburg University)

Abstract

Summary Rule induction methods are widely applied tools for mining large data bases. They are often used as a starting point in undirected data mining, i.e. when you do not know what specific patterns to look for. One form of rule induction methods are association rules which have their origin in market basket analysis. Since an evaluation of their results is often hard due to the mass of output, pruning methods are needed to turn the output of association rules into a manageable number of patterns. We will present some statistical measures and their depictions that are useful to assess the quality of an association rule. We will show plots portraying confidence and support for individual rules as well as for sets of rules as alternatives to displays currently used in commercial software. A new quality measure for association rules, the doc, will be introduced which overcomes some of the problems of support and confidence of association rules and can be visualised even for hundreds of rules in one diagram.

Suggested Citation

  • Heike Hofmann & Adalbert Wilhelm, 2001. "Visual Comparison of Association Rules," Computational Statistics, Springer, vol. 16(3), pages 399-415, September.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:3:d:10.1007_s001800100075
    DOI: 10.1007/s001800100075
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    Cited by:

    1. McNicholas, P.D. & Murphy, T.B. & O'Regan, M., 2008. "Standardising the lift of an association rule," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4712-4721, June.

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