How do you design clustering systems that support real-time anomaly detection alongside segmentation?

Clustering systems integrate anomaly detection by identifying low-density points or distance outliers during assignment.

Modern ML systems often combine clustering with anomaly detection. Points far from centroids or outside dense regions are flagged as anomalies. Systems like DBSCAN naturally support this, while K-Means uses distance thresholds. This dual-purpose architecture allows both segmentation and fraud detection in the same pipeline.