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Permanent Research Commons link: https://hdl.handle.net/10289/5551
Mixture model clustering proceeds by fitting a finite mixture of multivariate distributions to data, the fitted mixture density then being used to allocate the data to one of the components. Common model formulations assume that either all the attributes are continuous or all the attributes are categorical. In this paper, we consider options for model formulation in the more practical case of mixed data: multivariate data sets that contain both continuous and categorical attributes.