| Sign In to gain access to subscriptions and/or personal tools. |
DOI: 10.1191/0962280204sm372ra Mixture modelling for cluster analysisDepartment of Mathematics and the Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia, gjm{at}maths.uq.edu.au
Department of Mathematics, University of Queensland, Brisbane, Australia Cluster analysis via a finite mixture model approach is considered. With this approach to clustering, the data can be partitioned into a specified number of clusters g by first fitting a mixture model with g components. An outright clustering of the data is then obtained by assigning an observation to the component to which it has the highest estimated posterior probability of belonging; that is, the ith cluster consists of those observations assigned to the ith component (i = 1,..., g). The focus is on the use of mixtures of normal components for the cluster analysis of data that can be regarded as being continuous. But attention is also given to the case of mixed data, where the observations consist of both continuous and discrete variables.
This article has been cited by other articles:
|
||||||||||||
