How would you handle evolving data distributions in K-Means systems?

You handle distribution drift using periodic retraining, incremental updates, or sliding-window clustering.

In real systems, data distribution changes over time (concept drift). K-Means must be retrained periodically or updated using streaming methods. Otherwise, centroids become stale and unrepresentative.