Classification and analysis in supervised mixture-modelling

This paper describes an algorithm that is an extension of mixture-modelling to supervised clustering. It is demonstrated to be as accurate as current state-of-the-art machine learning algorithms across various data sets, and significantly more accurate than distance-based supervised clustering algorithms. Most significantly, it combines the classification itself with the calculation of rich information about the probabilities of class membership, the significance of attributes in relation to a classification, and the data space described by the data items and attributes.