Top 13 Data Mining Algorithms - Geeky Humans

The Expectation-Maximization (EM) algorithm is a way to find maximum-likelihood estimates for model parameters when the data is incomplete, or has missing data points, or has unobserved/hidden latent variables. This is an iterative way to approximate the maximum likelihood function. While maximum likelihood estimation can find the "best fit" model for a set of data, it does not work specifically well for incomplete data sets. The more complex Expectation-Maximization (EM) algorithm can find model parameters even if you have missing data. It works by selecting random values for the missing data points and using those guesses to estimate a second set of data.