The Expectation-Maximization (EM) algorithm is a statistical method used to find maximum likelihood estimates of parameters in probabilistic models, especially when the data involves latent variables. The algorithm alternates between an expectation step, where it estimates the missing data based on the current parameters, and a maximization step, where it updates the parameters to maximize the likelihood given the estimated data. This iterative process is particularly useful in clustering tasks, allowing for more accurate groupings by handling incomplete or hidden data effectively.
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