How do you handle dynamic cluster updates when new data continuously arrives?

Dynamic clustering updates models incrementally using online learning or periodic re-clustering.

Static clustering fails when data evolves. Systems either update clusters incrementally (online K-Means) or recompute periodically. Hybrid systems combine both: small updates continuously and full retraining at intervals. This ensures freshness without instability.