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Technical Report Details

Date 18-10-2006
Number DISI-TR-06-19
Title A survey of kernel and spectral methods for clustering
Authors Maurizio Filippone, Francesco Camastra, Francesco Masulli, Stefano Rovetta
Bibtex Entry @techreport{FilipponeTR_PR06, author = "Maurizio Filippone and Francesco Camas
E-mail filippone@disi.unige.it
Link ftp://ftp.disi.unige.it/person/FilipponeM/Publications/tech_rep_pr06.pdf
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: spectral and kernel 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 kernel clustering methods presented 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 extension of kernel K-means clustering algorithm.
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