Learning in parallel universes

Authors:
Bernd Wiswedel;Frank Höppner;Michael R. Berthold
Affiliations:
Department of Computer and Information Science, University of Konstanz, Konstanz, Germany;Faculty of Business (Information Systems), Ostfalia University of Applied Sciences, Wolfenbüttel, Germany;Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
Venue:
Data Mining and Knowledge Discovery
Year:
2010

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Abstract

We discuss Learning in parallel universes as a learning concept that encompasses the simultaneous analysis from multiple descriptor spaces. In contrast to existing approaches, this approach constructs a global model that is based on only partially applicable, local models in each descriptor space. We present some application scenarios and compare this learning strategy to other approaches on learning in multiple descriptor spaces. As a representative for learning in parallel universes we introduce different extensions to a family of unsupervised fuzzy clustering algorithms and evaluate their performance on an artificial data set and a benchmark of 3D objects.