PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Ranking Interesting Subgroups
Stefan Rüping
In: 26th International Conference on Machine Learning (ICML 2009), 14-18 June 2009, Montreal, Canada.

Abstract

Subgroup discovery is the task of identifying the top k patterns in a database with most significant deviation in the distribution of a target attribute Y . Subgroup discovery is a popular approach for identifying interesting patterns in data, because it combines statistical significance with an understandable representation of patterns as a logical formula. However, it is often a problem that some subgroups, even if they are statistically highly significant, are not interesting to the user. We present an approach based on the work on ranking Support Vector Machines that ranks subgroups with respect to the user’s concept of interestingness, and finds more interesting subgroups. This approach can significantly increase the quality of the subgroups.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:6146
Deposited By:Stefan Rueping
Deposited On:08 March 2010