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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 clus..
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