Interactive Use of Problem Knowledge for Clustering and Decision Making

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.