EM

Abstract The expectation-maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood (ML) estimates, useful in a variety of incomplete-data problems. In particular, the EM algorithm simplifies considerably the problem of fitting finite mixture models by ML, where mixture models are used to model heterogeneity in cluster analysis and pattern recognition contexts. The EM algorithm has a number of appealing properties, including its numerical stability, simplicity of implementation, and reliable global convergence. There are also extensions of the EM algorithm to tackle complex problems in various data mining applications. It is, however, highly desirable if its simplicity and stability can be preserved.