Expectation Step and Maximization Step

EM iteratively computes and maximizes likelihood to improve model accuracy, ensuring reliable clustering results.

Expectation maximization is an iterative method. It starts with an initial parameter guess. The parameter values are used to compute the likelihood of the current model. This is the Expectation step. The parameter values are then recomputed to maximize the likelihood. This is the Maximization step. The new parameter estimates are used to compute a new expectation and then they are optimized again to maximize the likelihood. This iterative process continues until model convergence.