A Bayesian approach to clustering and classification

The author describes a classification approach and associated algorithms designed for use with continuous but non-Gaussian data. The probability density function for each class is modeled as a mixture of Gaussian distributions. The clustering algorithm estimates the means the covariances of the component Gaussian distributions for each class. A classification rule based on the mixture model is presented