A Partial EM Algorithm for Model‐based Clustering with Highly Diverse Missing Data Patterns

The expectation-maximization (EM) algorithm for incomplete data with highly diverse missing data patterns can be computationally expensive. A partial expectation-maximization (PEM) algorithm is developed to ease this computational burden. This PEM algorithm circumvents the need for a traditional E-step by performing a partial E-step that reduces the Kullback-Leibler divergence between the conditional distribution of the missing data and the distribution of the missing data given the observed data. The PEM and EM algorithms are compared in terms of computation time and convergence on simulated data. The PEM algorithm is illustrated using a latent Gaussian mixture model to cluster a white bread sensory analysis dataset.