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Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision

arXiv 2608.12027 ↗First posted 12 Aug 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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 relation 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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References · 21

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9 more
  1. 2020AutoEmbedder: A semi-supervised DNN embedding system for clusteringcited by 8
  2. 2023Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directionscited by 8
  3. 2018Semi-crowdsourced Clustering with Deep Generative Modelscited by 7
  4. 2023Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approachcited by 5
  5. 2023Efficient Multi-View Graph Clustering with Local and Global Structure Preservationcited by 5
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  7. 2021Fuzzy Discriminant Clustering with Fuzzy Pairwise Constraintscited by 3
  8. 2023ConstraintMatch for Semi-constrained Clusteringcited by 2
  9. 2025Angular Constraint Embedding via SpherePair Loss for Constrained Clusteringcited by 2

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