PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On Subgroup Discovery in Numerical Domains
Henrik Grosskreutz and Stefan Rüping
Data Mining and Knowledge Discovery Volume 19, Number 2, 2009.

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

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6931
Deposited By:Henrik Grosskreutz
Deposited On:29 April 2010