Scalable Uncertainty Quantification for Black-Box Density-Based Clustering

• URL: http://arxiv.org/abs/2603.03188v1
• Date: Tue, 03 Mar 2026 17:46:49 GMT
• Title: Scalable Uncertainty Quantification for Black-Box Density-Based Clustering
• Authors: Nicola Bariletto, Stephen G. Walker,
• Abstract summary: We introduce a novel framework for uncertainty quantification in clustering.<n>By combining the martingale posterior paradigm with density-based clustering, uncertainty in the estimated density is naturally propagated to the clustering structure.
• Score: 3.5808917363708743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel framework for uncertainty quantification in clustering. By combining the martingale posterior paradigm with density-based clustering, uncertainty in the estimated density is naturally propagated to the clustering structure. The approach scales effectively to high-dimensional and irregularly shaped data by leveraging modern neural density estimators and GPU-friendly parallel computation. We establish frequentist consistency guarantees and validate the methodology on synthetic and real data.

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