The Classification and Mixture Maximum Likelihood Approaches to Cluster Analysis

摘要

A review is undertaken of two maximum likelihood approaches to cluster analysis, the so-called classification and mixture maximum likelihood methods. The basic assumptions of the two approaches and their associated properties are contrasted, in particular for multivariate normal component distributions. The problem of deciding how many clusters there are is discussed for each approach. Also, an account is given of the relative efficiency of the mixture approach to clustering. (Author)