An Entropy criterion for assessing the number of clusters in a mixture model
Résumé
In this paper, we consider an entropy criterion to estimate the number of clusters arising from a mixture model. This criterion is derived from a relation linking the likelihood and the classification likelihood of a mixture. Its performances are investigated through Monte-Carlo numerical experiments and show favourable results as compared with other classical criteria.