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.
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Szepannek, G., Luebke, K., Weihs, C. (2005). Understanding Patterns with Different Subspace Classification. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_12
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DOI: https://doi.org/10.1007/11510888_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
Online ISBN: 978-3-540-31891-0
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