A comparison of the mixture and classification approaches to cluster analysis

This paper examines the relative performance of two commonly used clustering methods based on maximum likelihood in the context of classifying a sample of observations of unknown origin arising from two normal populations with a common covariance matrix. the associated properties of the two methods are compared by conducting a series of simulation experiments under both mixture and separate sampling schemes.