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. (doi: 10.1016/j.patcog.2007.05.018)

Abstract

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.

• Item Type: Articles
• Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 41(1), 2008, DOI: 10.1016/j.patcog.2007.05.018
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Filippone, Dr Maurizio
• Authors: Filippone, M., Camastra, F., Masulli, F., and Rovetta, S.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Pattern Recognition
• Publisher: Elsevier
• ISSN: 0031-3203
• Published Online: 19 June 2007
• Copyright Holders: Copyright © 2008 Elsevier
• First Published: First published in Pattern Recognition 41(1):176-190
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher