How does K-Means handle convergence?

Updated May 15, 2026

Short answer

K-Means converges when centroids stop changing significantly.

Deep explanation

The algorithm iterates until centroid positions stabilize or max iterations are reached.

Real-world example

Stable customer grouping after several iterations.

Common mistakes

Assuming convergence guarantees global optimum.

Follow-up questions

Does K-Means always converge?

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