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Pairwise clustering based on the mutual-information criterion
2016
Neurocomputing
Pairwise clustering methods partition a dataset using pairwise similarity between data-points. The pairwise similarity matrix can be used to define a Markov random walk on the data points. This view forms a probabilistic interpretation of spectral clustering methods. We utilize this probabilistic model to define a novel clustering cost function that is based on maximizing the mutual information between consecutively visited clusters of states of the Markov chain defined by the similarity
doi:10.1016/j.neucom.2015.12.025
fatcat:sf5pnf47ove7rlzfa6vifrceuy