How do modern clustering systems handle dynamic data drift?

They use incremental updates, sliding windows, and adaptive clustering thresholds.

In real-world systems, data distributions evolve over time (concept drift). Clustering systems handle this by continuously updating centroids, decaying old data influence, or using sliding window-based clustering. Streaming algorithms like online KMeans or incremental DBSCAN variants adapt to changes without retraining from scratch.