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The EM (Expectation Maximization) Algorithm
The k-means algorithm has two steps at each iteration:
(E-step): Given the current cluster centers, each object is assigned to the cluster whose center is closest to the object: An object is
expected to belong to the closest cluster
(M-step): Given the cluster assignment, for each cluster, the algorithm
adjusts the center
the sum of distance
from the objects assigned to this cluster and the new center is minimized
The (EM) algorithm:
A framework to approach maximum likelihood or maximum a posteriori estimates of parameters in statistical models.
assigns objects to clusters according to the current fuzzy clustering or parameters of probabilistic clusters
finds the new clustering or parameters that minimize the sum of squared error (SSE) or the expected likelihood