In this recipe, we will explore Spark's implementation of expectation maximization (EM) GaussianMixture(), which calculates the maximum likelihood given a set of features as input. It assumes a Gaussian mixture in which each point can be sampled from K number of sub-distributions (cluster memberships).
Using Gaussian Mixture and Expectation Maximization (EM) in Spark to classify data
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