Using multi-SOMs and multi-neural-gas as neural classifiers

Authors:
Nils Goerke;Alexandra Scherbart
Affiliations:
Div. of Neural Computation, Dept. of Computer Sciene, University of Bonn, Bonn, Germany;Div. of Neural Computation, Dept. of Computer Sciene, University of Bonn, Bonn, Germany
Venue:
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Year:
2006

Citing 3
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Abstract

Within this paper we present the extension of two neural network paradigms for clustering tasks. The Self Organizing feature Maps (SOM) are extended to the Multi SOM approach, and the Neural Gas is extended to a Multi Neural Gas. Some common cluster analysis coefficients (Silhouette Coefficient, Gap Statistics, Calinski-Harabasz Coefficient)have been adapted for the new paradigms. Both new neural clustering methods are described and evaluated briefly using exemplary data sets.