A Markov random field model for mode detection in cluster analysis

A statistical clustering approach is proposed, based on Markov random field models. A discrete field derived from the raw data set is considered as a field of measures. A hidden field, computed using a new potential function, is used to detect the modes that correspond to domains of high local concentrations of observations. Results obtained on artificially generated and real data sets demonstrate the efficiency of this new approach for unsupervised pattern classification.

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