What is the impact of initialization variance on model reproducibility?

Random initialization introduces variability in final clusters unless controlled using fixed seeds or deterministic initialization.

Because K-Means converges to local minima, different initial centroids produce different solutions. Setting random seeds or using K-Means++ reduces variance but does not eliminate it entirely.