Generic Pattern Trees for Exhaustive Exceptional Model Mining

  • Florian Lemmerich
  • Martin Becker
  • Martin Atzmueller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7524)


Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search.


exceptional model mining subgroup discovery 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Florian Lemmerich
    • 1
  • Martin Becker
    • 1
  • Martin Atzmueller
    • 2
  1. 1.Artificial Intelligence and Applied Computer Science GroupUniversity of WürzburgWürzburgGermany
  2. 2.Knowledge & Data Engineering GroupUniversity of KasselKasselGermany

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