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In this paper, we propose an algorithm that performs fuzzy clustering and feature weighting simultaneously and in an unsupervised manner. The feature set is divided into logical subsets of features, and a degree of relevance is dynamically assigned to each subset based on its partial degree of dissimilarity. The proposed algorithm is computationally and implementationally simple, and learns a different set of feature weights for each cluster. The cluster dependent feature weights have two advantages. First, they help in partitioning the data set into more meaningful clusters. Second, they can be used as part of a more complex learning system to enhance its learning behavior. The performance of the proposed algorithm is illustrated by using it to segment color images.