Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision

• URL: http://arxiv.org/abs/2608.12027v1
• Date: Wed, 12 Aug 2026 13:07:19 GMT
• Title: Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision
• Abstract summary: We introduce uncertainty-aware constrained clustering (UPCC)<n>We define a canonical aleatoric target through a heterogeneous observation process and analyze its conditional identifiability.<n>We build ECI-PP, an estimator-corrector--integrator framework that refines imperfect supervision via belief estimation, correction, and reliability-aware integration.
• Score: 14.00293068731373
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Pairwise constrained clustering typically relies on hard must-link/cannot-link labels, whereas realistic pairwise supervision may be real-valued and entangle intrinsic ambiguity, expert judgment, and stochastic corruption. Existing deep constrained clustering (DCC) methods mainly target hard, expert-agnostic constraints, treating soft labels mostly numerically rather than semantically. We formalize this setting as uncertainty-aware probabilistic constrained clustering (UPCC), defining a canonical aleatoric target through a heterogeneous observation process and analyzing its conditional identifiability. We introduce ProbPair, an angular pairwise objective for probabilistic relations, and build ECI-PP, an estimator--corrector--integrator framework that refines imperfect supervision via belief estimation, correction, and reliability-aware integration. Across challenging probabilistic supervision settings, experiments on diverse benchmarks show that ECI-PP outperforms state-of-the-art DCC methods and remains robust with a shared default configuration.

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