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An approach to clustering and decision making is presented where a prior problem knowledge is inserted interactively. The problem knowledge inserted is in the form of subcategory mean vectors and covariance matrices and in the expert's confidence that these means and covariances accurately characterize the category. Then observations of patterns from the category are used to update these a priori supplied means and covariances. The extent to which new observations update the a priori values depends upon the expert's a priori confidence.