An EM Stopping Rule for Avoiding Degeneracy in Gaussian-based Clustering with Missing Data

Missing data frequency increases with the growing size of multivariate modern datasets. In Gaussian model-based clustering, the EM algorithm easily takes into account such data but the degeneracy problem is dramatically aggravated during the EM runs: parameter de- generacy is quite slow and also more frequent than with complete data. Consequently, parameter degenerated solutions may be confused with valuable parameter solutions and, in addition, computing time may be wasted through wrong runs. In this work, a simple and low informa- tional condition on the latent partition allows to propose a very simple partition-based stopping rule of EM which shows good behavior on nu- merical experiments.