Unsupervised clustering with growing cell structures

A neural network model is presented which is able to detect clusters of similar patterns. The patterns are n-dimensional real number vectors according to an unknown probability distribution P(X). By evaluating sample vectors according to P(X) a two-dimensional cell structure is gradually built up which models the distribution. Through removal of cells corresponding to areas with low probability density the structure is then split into several disconnected substructures. Each of them identifies one cluster of similar patterns. Not only is the number of clusters determined but also an approximation of the probability distribution inside each cluster. The accuracy of the cluster description increases linearly with the number of evaluated sample vectors.<
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