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K-Means Clustering
K-Means Clustering
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The k-means algorithm alternates between assigning each point to the nearest centroid and recomputing centroids as cluster means. Which statement about convergence is correct?
Hide and think first
A.
K-means converges to the global optimum for any initialization
B.
K-means may oscillate between two partitions and never converge
C.
K-means is guaranteed to converge in at most
O
(
n
)
iterations where
n
is the number of data points, regardless of initialization
D.
The objective is non-increasing at each step, so the algorithm converges in finitely many steps, but possibly to a local minimum
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