An Experimental Comparison of Kernel Clustering Methods

Authors:
Maurizio Filippone;Francesco Masulli;Stefano Rovetta
Affiliations:
Department of Computer Science, University of Sheffield, Sheffield, United Kingdom;Department of Computer and Information Sciences, University of Genoa, and CNISM, Genova Research Unit, Genova, Italy and Center for Biotechnology, Temple University, Philadelphia, USA;Department of Computer and Information Sciences, University of Genoa, and CNISM, Genova Research Unit, Genova, Italy
Venue:
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Year:
2009

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Abstract

In this paper, we compare the performances of some among the most popular kernel clustering methods on several data sets. The methods are all based on central clustering and incorporate in various ways the concepts of fuzzy clustering and kernel machines. The data sets are a sample of several application domains and sizes. A thorough discussion about the techniques for validating results is also presented. Results indicate that clustering in kernel space generally outperforms standard clustering, although no method can be proven to be consistently better than the others.