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Multi-class Correlated Pattern Mining

  • Siegfried Nijssen
  • Joost N. Kok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3933)

Abstract

To mine databases in which examples are tagged with class labels, the minimum correlation constraint has been studied as an alternative to the minimum frequency constraint. We reformulate previous approaches and show that a minimum correlation constraint can be transformed into a disjunction of minimum frequency constraints. We prove that this observation extends to the multi-class χ 2 correlation measure, and thus obtain an efficient new O(n) prune test. We illustrate how the relation between correlation measures and minimum support thresholds allows for the reuse of previously discovered pattern sets, thus avoiding unneccessary database evaluations. We conclude with experimental results to assess the effectivity of algorithms based on our observations.

Keywords

Receiver Operating Characteristic Curve Frequent Pattern Minimum Support Correlation Measure Target Class 
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

  • Siegfried Nijssen
    • 1
  • Joost N. Kok
    • 2
  1. 1.Albert-Ludwidgs-UniversitätFreiburg im BreisgauGermany
  2. 2.LIACSLeiden UniversityLeidenThe Netherlands

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