Matrix Neuro-Fuzzy Self-Organizing Clustering Network
2011
Yevgeniy Bodyanskiy, Valentyna Volkova, Mark Skuratov

In this article the problem of clustering massive data sets, which are represented in the matrix form, is considered. The article represents the 2-D self-organizing Kohonen map and its self-learning algorithms based on the winner-take-all (WTA) and winner-take-more (WTM) rules with Gaussian and Epanechnikov functions as the fuzzy membership functions, and without the winner. The fuzzy inference for processing data with overlapping classes in a neural network is introduced. It allows one to estimate membership levels for every sample to every class. This network is the generalization of a vector neuro- and neuro-fuzzy Kohonen network and allows for data processing as they are fed in the on-line mode.


Atslēgas vārdi
Fuzzy clustering, matrix, self-organizing map
DOI
10.2478/v10143-011-0042-1
Hipersaite
http://www.degruyter.com/view/j/acss.2011.45.issue--1/v10143-011-0042-1/v10143-011-0042-1.xml?format=INT

Bodyanskiy, Y., Volkova, V., Skuratov, M. Matrix Neuro-Fuzzy Self-Organizing Clustering Network. Informācijas tehnoloģija un vadības zinātne. Nr.49, 2011, 54.-58.lpp. ISSN 1407-7493. Pieejams: doi:10.2478/v10143-011-0042-1

Publikācijas valoda
English (en)
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