How do you design clustering systems that adapt to concept drift in production environments?

Adaptive clustering systems detect distribution shifts and update clusters incrementally or via retraining.

Concept drift occurs when underlying data distribution changes over time. Clustering systems must detect drift using statistical monitoring (e.g., KL divergence, centroid movement). Once drift is detected, models are updated using incremental updates or full retraining. Hybrid systems combine both approaches for stability and responsiveness.