Pattern discovery algorithms typically produce many interesting patterns. In most cases, patterns are reported based on their individual merits, and little attention is given to the interestingness of a pattern in the context of other patterns reported. In this paper, we propose filtering the returned set of patterns based on a number of quality measures for pattern sets. We refer to a small subset of patterns that optimises such a measure as a pattern team. A number of quality measures, both supervised and unsupervised, is proposed. We analyse to what extent each of the measures captures a number of ‘intuitions’ users may have concerning effective and informative pattern teams. Such intuitions involve qualities such as independence of patterns, low overlap, and combined predictiveness.


Quality Measure Association Rule Mining Decision Table Pattern Discovery Interestingness Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arno J. Knobbe
    • 1
    • 2
  • Eric K. Y. Ho
    • 1
  1. 1.KiminkiiHoutenThe Netherlands
  2. 2.Utrecht UniversityUtrechtThe Netherlands

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