Expectation-Maximization (EM) clustering is a robust algorithm used for becoming combination fashions to information, significantly in conditions the place the info could also be higher represented by a number of underlying distributions. It’s an iterative algorithm that alternates between an expectation (E) step, the place it computes the anticipated worth of the latent variables given the noticed information and present parameter estimates, and a maximization (M) step, the place it maximizes the probability operate to replace the parameters.
Right here’s an in depth overview of the Expectation-Maximization clustering algorithm:
