%0 Journal Article
%T Merging the components of a finite mixture using posterior probabilities
%A Gl¨°ria Mateu-Figueras
%A Josep A Mart¨ªn-Fern¨¢ndez
%A Marc Comas-Cuf¨ª
%J Statistical Modelling
%@ 1477-0342
%D 2019
%R 10.1177/1471082X17735919
%X Methods in parametric cluster analysis commonly assume data can be modelled by means of a finite mixture of distributions. However, associating each mixture component to one cluster is frequently misleading because different mixture components can overlap, and then, associated clusters can overlap too suggesting a unique cluster. A number of approaches have already been proposed to construct the clusters by merging components using the posterior probabilities. This article presents a generic approach for building a hierarchy of mixture components that integrates and generalizes some techniques proposed earlier in the literature. Using this proposal, two new techniques based on the log-ratio of posterior probabilities are introduced. Moreover, to decide the final number of clusters, two new methods are presented. Simulated and real datasets are used to illustrate this methodology
%K Hierarchical clustering
%K Log-ratio
%K merging components
%K mixture model
%K model-based clustering
%K simplex
%U https://journals.sagepub.com/doi/full/10.1177/1471082X17735919