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Sound Multi-objective Feature Space Transformation for Clustering

  • In this work we propose a novel, generalized framework for feature space transformation in unsupervised knowledge discovery settings. Unsupervised feature space transformation inherently is a multi-objective optimization problem. In order to facilitate data exploration, transformations should increase the quality of the result and should still preserve as much of the original data set information as possible. We exemplify this relationship on the problem of data clustering. First, we show that existing approaches to multi-objective unsupervised feature selection do not pose the optimization problem in an appropriate way. Furthermore, using feature selection only is often not sufficient for real-world knowledge discovery tasks. We propose a new, generalized framework based on the idea of information preservation. This framework enables feature selection as well as feature construction for unsupervised learning. We compare our method against existing approaches on several real world data sets.

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Metadaten
Author:Ingo Mierswa, Michael Wurst
URN:https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-499
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2011/04/28
Contributing Corporation:Department of Computer Science University of Dortmund
Release Date:2011/04/28
Source:LWA 2006: Lernen - Wissensentdeckung - Adaptivität, Hildesheim, 9. - 11. Oktober 2006
PPN:Link zum Katalog
Contributor:Althoff, Klaus-Dieter
Institutes:Fachbereich IV / Informatik
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Licence (German):License LogoDeutsches Urheberrecht