Clustering using neural networks and Kullback-Leibler divergency

In this work we develop a clustering algorithm based on Kullback-Leibler divergence as the dissimilarity measurement. That measure is used with an algorithm that uses the classical vector quantization with competitive neural networks to perform the clustering of spatially complex data sets. The algorithm is also presented as an alternative tool to obtain a model based on Gaussian mixture of complex data sets. The clustering algorithm is tested with several data sets generated artificially. All sets in the data set is also modelled with a Gaussian mixture using the proposed algorithm.