Fast Subgroup Discovery for Continuous Target Concepts

  • Martin Atzmueller
  • Florian Lemmerich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5722)


Subgroup discovery is a flexible data mining method for a broad range of applications. It considers a given property of interest (target concept), and aims to discover interesting subgroups with respect to this concept. In this paper, we especially focus on the handling of continuous target variables and describe an approach for fast and efficient subgroup discovery for such target concepts. We propose novel formalizations of effective pruning strategies for reducing the search space, and we present the SD-Map* algorithm that enables fast subgroup discovery for continuous target concepts. The approach is evaluated using real-world data from the industrial domain.


Quality Function Optimistic Estimate Target Variable Target Concept Pruning Strategy 
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 2009

Authors and Affiliations

  • Martin Atzmueller
    • 1
  • Florian Lemmerich
    • 1
  1. 1.Department of Computer Science VIUniversity of WürzburgWürzburgGermany

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