Expectation Maximization (EM) Clustering Algorithm

algorithm
machine-learning
clustering-algorithm
data-mining
expectation-maximization
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We know that K-Means calculates the mean of all the points in a cluster in every iteration, in order to proceed towards convergence. The Expectation Maximization Clustering algorithm is much more robust than K-Means, as it uses two parameters, Mean and Standard Deviation to define a particular cluster. This simple addition of calculating the Standard Deviation, helps the EM algorithm do well in a lot of fail cases of K-Means.

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