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We propose a novel image database categorization approach using a possibilistic clustering algorithm. The proposed algorithm is based on a robust data modeling using the Generalized Dirichlet (GD) finite mixture and generates two types of membership degrees. The first one is a posterior probability that indicates the degree to which the point fits the estimated distribution. The second membership...
Mixture-model-based clustering has become a popular approach in many data analysis problems for its statistical properties and the implementation simplicity of the EM algorithm. However the computation time of the EM algorithm and its variants increases significantly with the sample size. For large data sets, performing clustering on grouped data constitutes an efficient alternative to speed up the...
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