Abstract
This paper introduces a probabilistic self-organizing map for topographic clustering, analysis of categorical data. By considering a parsimonious mixture model, we present a new probabilistic Self-Organizing Map (SOM). The estimation of parameters is performed by the EM algorithm. Contrary to SOM, our proposed learning algorithm optimizes an objective function. Its performance is evaluated on real datasets.Keywordsunsupervised learningmixture modelsSelf-Organizing Mapscategorical data
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