Mixture Modeling and Information Criteria for Discovering Patterns in Continuous Data

This study addresses the adequacy of some theoretical information criteria when using finite mixture modelling on discovering patterns in continuous data. In fact, the selection of an adequate number of clusters is a key issue in deriving complex mixture structures and it is desirable that information criteria used for this end are effective. In order to select among several information criteria, which may support the selection of the correct number of clusters, we conduct a simulation study that is intended to determine which information criteria are more appropriate for mixture model selection when considering data sets with only continuous clustering base variables. As a result, the criterion BIC shows a better performance, that is, it indicates the correct number of the simulated cluster structures more often, when referring to mixtures of continuous clustering base variables.