A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph

Abstract

Flexible and efficient variants of the Self Organizing Map algorithm have been proposed for non vector data, including, for example, the dissimilarity SOM (also called the Median SOM) and several kernelized versions of SOM. Although the first one is a generalization of the batch version of the SOM algorithm to data described by a dissimilarity measure, the various versions of the second ones are stochastic SOM. We propose here to introduce a batch version of the kernel SOM and to show how this one is related to the dissimilarity SOM. Finally, an application to the classification of the vertices of a graph is proposed and the algorithms are tested and compared on a simulated data set.