%0 Journal Article
%T Matrix Neuro-Fuzzy Self-Organizing Clustering Network
%A Yevgeniy Bodyanskiy
%A Valentyna Volkova
%A Mark Skuratov
%J Scientific Journal of Riga Technical University. Computer Sciences
%D 2011
%I 
%R 10.2478/v10143-011-0042-1
%X 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.
%K Fuzzy clustering
%K matrix
%K self-organizing map
%U http://versita.metapress.com/content/q2v43w3w5kj35685/?p=7f1e98c8565a441098048eea27120978&pi=7