k-means

How does the EM algorithm (in the context of GMM) compare to K-Means?

K-Means aims to minimize the Within Cluster Sum of Squares, while EM aims to maximize the likelihood of an underlying probability distribution.

Pros: Do not have to specify the number of clusters before running the algorithm

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