Expectation-Maximization (EM) clustering is a strong algorithm used for turning into mixture fashions to data, considerably in circumstances the place the data may be increased represented by plenty of underlying distributions. It’s an iterative algorithm that alternates between an expectation (E) step, the place it computes the anticipated value of the latent variables given the observed data and current parameter estimates, and a maximization (M) step, the place it maximizes the likelihood function to interchange the parameters.
Proper right here’s an in depth overview of the Expectation-Maximization clustering algorithm:
