Population mixture models and clustering algorithms

The problem of clustering individuals is considered within the context of a mixture of distributions, where a parametric family of distributions is considered, a set of parameter values being associated with each population.

Abstract

Abstract The problem of clustering individuals is considered within the context of a mixture of distributions. A modification of the usual approach to population mixtures is employed. As usual, a parametric family of distributions is considered, a set of parameter values being associated with each population. In addition, with each observation is associated an identification parameter, Indicating from which population the observation arose. Theresulting likelihood function is interpreted in terms of the conditional probability density of a sample from a mixture of populations, given the identification parameter of each observation. Clustering algorithms are obtained by applying a method of iterated maximum likelihood to this like-lihood function. Keywords: mixture of distributionscluster analysisisodata procedurek-means proceduremahalanobis distancemultivariate analysis