DocumentCode
2776016
Title
Classification using Multi-SOMs and Multi-Neural Gas
Author
Goerke, Nils ; Scherbart, Alexandra
Author_Institution
Division of Neural Computation, Department of Computer Science, University of Bonn, Roemerstr. 164, D-53117 Bonn, Germany. email: goerke@nero.uni-bonn.de
fYear
2006
fDate
16-21 July 2006
Firstpage
3895
Lastpage
3902
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.
Keywords
Clustering methods; Neural networks; Neurons; Radial basis function networks; Resonance; Self organizing feature maps; Shape; Statistical analysis; Supervised learning; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Print_ISBN
0-7803-9490-9
Type
conf
DOI
10.1109/IJCNN.2006.246887
Filename
1716635
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