Understanding patterns with different subspace classification

Authors:
Gero Szepannek;Karsten Luebke;Claus Weihs
Affiliations:
Department of Statistics, University of Dortmund;Department of Statistics, University of Dortmund;Department of Statistics, University of Dortmund
Venue:
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2005

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Abstract

By identifying characteristic regions in which classes are dense and also relevant for discrimination a new, intuitive classification method is set up. This method enables a visualized result so the user is provided with an insight into the data with respect to discrimination for an easy interpretation. Additionally, it outperforms Decision trees in a lot of situations and is robust against outliers and missing values.