The problem of fuzzy clustering of multivariate observations is considered and a group of Kohonen neural network adaptive self-learning algorithms is proposed. The algorithms allow for online possibilistic fuzzy clustering with variable fuzziness levels and are computationally simple and flexible when operating under a priori uncertainty about the nature of data distribution in clusters.
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