An Algorithm for Nonsupervised Pattern Classification

Abstract

An algorithm for classifying a data set into an initially unknown number of categories is presented. It is composed of procedure for selecting initial points, a mode estimation procedure, and a classification rule. An integer valued function is defined on the sample space and a gradient search technique is used for estimating its modes. A procedure for mode estimation in the case of an infinite data set is also proposed. Sufficient conditions for the convergence to the neighborhood of the modes have been stated. The algorithm was used for clustering multicategory artificially generated data sets and was compared with an optimal classification scheme.