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.
This is an extended abstract of an article published in the Data Mining and Knowledge Discovery journal [1].
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Grosskreutz, H., RĂ¼ping, S.: On Subgroup Discovery in Numerical Domains. Data Mining and Knowledge Discovery (2009) doi: 10.1007/s10618-009-0136-3
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© 2009 Springer-Verlag Berlin Heidelberg
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Grosskreutz, H., RĂ¼ping, 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. https://doi.org/10.1007/978-3-642-04180-8_15
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DOI: https://doi.org/10.1007/978-3-642-04180-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04179-2
Online ISBN: 978-3-642-04180-8
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