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 …