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A survey of kernel and spectral methods for clustering

Filippone, M., Camastra, F., Masulli, F. and Rovetta, S. (2008) A survey of kernel and spectral methods for clustering. Pattern Recognition, 41 (1). pp. 176-190. ISSN 0031-3203


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Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods. The aim of this paper is to present a survey of kernel and spectral clustering methods, two approaches able to produce nonlinear separating hypersurfaces between clusters. The presented kernel clustering methods are the kernel version of many classical clustering algorithms, e.g., K-means, SOM and neural gas. Spectral clustering arise from concepts in spectral graph theory and the clustering problem is configured as a graph cut problem where an appropriate objective function has to be optimized. An explicit proof of the fact that these two paradigms have the same objective is reported since it has been proven that these two seemingly different approaches have the same mathematical foundation. Besides, fuzzy kernel clustering methods are presented as extensions of kernel K-means clustering algorithm. (C) 2007 Pattem Recognition Society. Published by Elsevier Ltd. All rights reserved.

Item Type: Article
Copyright, Publisher and Additional Information: © 2007 Elsevier. This is an author produced version of a paper subsequently published in Pattern Recognition. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: partitional clustering; Mercer kernels; kernel clustering; kernel fuzzy clustering; spectral clustering
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Miss Anthea Tucker
Date Deposited: 28 Apr 2009 15:43
Last Modified: 08 Feb 2013 16:58
Published Version: http://dx.doi.org/10.1016/j.patcog.2007.05.018
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: 10.1016/j.patcog.2007.05.018
URI: http://eprints.whiterose.ac.uk/id/eprint/8536

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