On Subgroup Discovery in Numerical Domains

  • Henrik Grosskreutz
  • Stefan Rüping
Conference paper

DOI: 10.1007/978-3-642-04180-8_15

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)
Cite this paper as:
Grosskreutz H., Rüping S. (2009) On Subgroup Discovery in Numerical Domains. In: Buntine W., Grobelnik M., Mladenić D., Shawe-Taylor J. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2009. Lecture Notes in Computer Science, vol 5781. Springer, Berlin, Heidelberg

Abstract

Subgroup discovery is a Knowledge Discovery task that aims at finding subgroups of a population with high generality and distributional unusualness. While several subgroup discovery algorithms have been presented in the past, they focus on databases with nominal attributes or make use of discretization to get rid of the numerical attributes. In this paper, we illustrate why the replacement of numerical attributes by nominal attributes can result in suboptimal results. Thereafter, we present a new subgroup discovery algorithm that prunes large parts of the search space by exploiting bounds between related numerical subgroup descriptions. The same algorithm can also be applied to ordinal attributes. In an experimental section, we show that the use of our new pruning scheme results in a huge performance gain when more that just a few split-points are considered for the numerical attributes.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Henrik Grosskreutz
    • 1
  • Stefan Rüping
    • 1
  1. 1.Fraunhofer IAIS, Schloss BirlinghovenSankt AugustinGermany

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