How do you design clustering systems that prevent noisy data from corrupting clusters in production?

Noise robustness is achieved using outlier detection, robust distance metrics, and preprocessing filters.

Noisy data can significantly distort clustering results, especially in centroid-based methods. Production systems mitigate this using preprocessing pipelines that detect outliers using statistical thresholds or density-based methods. Robust clustering algorithms like DBSCAN or trimmed K-Means reduce sensitivity to noise. Additionally, feature normalization and anomaly filtering ensure cleaner inputs.