Abstract
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. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Litinskii, L. B., & Romanov, D. E. (2006). Neural network clustering based on distances between objects. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4132 LNCS-II, pp. 437–443). Springer Verlag. https://doi.org/10.1007/11840930_45
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