Neural Network Clustering Based on Distances Between Objects

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We present an algorithm of clustering of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. The result of clustering does not depend on starting conditions. Our algorithm makes it possible to give an idea about classes that really exist in the empirical data. The results of computer simulations are presented.
7 pages,4 figures, presentation on ICANN (Athens, Greece, 2006)

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Computer Vision and Pattern Recognition, Neural and Evolutionary Computing

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http://salesiana.dossiersoluciones.com/handle/123456789/114862

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/0608115
 http://arxiv.org/abs/cs/0608115

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arXiv