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.
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Goerke, N., Scherbart, A. (2006). Using Multi-SOMs and Multi-Neural-Gas as Neural Classifiers. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_20
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DOI: https://doi.org/10.1007/11790853_20
Publisher Name: Springer, Berlin, Heidelberg
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