Semi-Supervised Hyperbolic Hierarchical Clustering with Set-Level Structural Priors

2026-05-31

AI Summary

This work addresses the limitation of traditional semi-supervised hierarchical clustering, which relies solely on leaf-node constraints and thus struggles to effectively guide the formation of non-leaf hierarchical structures, often yielding trees inconsistent with ground-truth hierarchies. To overcome this, the authors propose a novel semi-supervised hyperbolic hierarchical clustering method that leverages set-level structural priors. Specifically, leaf-level constraints are transformed into semantically coherent sample sets, which serve as soft priors for subtree levels to guide end-to-end tree optimization. The approach innovatively treats sets as fundamental modeling units and integrates constrained consistent embedding, set partitioning, inter-set similarity estimation, and continuous tree optimization in hyperbolic space to enable effective supervision of non-leaf structures. Evaluated on eleven benchmark datasets, the method significantly outperforms existing baselines in both label consistency and tree quality metrics.

Abstract

Semi-supervised hierarchical clustering aims to learn a tree structure consistent with data patterns and user-provided supervision. Supervision is usually given as leaf-level relations, such as pairwise must-link/cannot-link constraints or triplet-wise must-link-before constraints. Although useful for regulating local sample relations, such supervision does not directly indicate which samples should form coherent subtrees. Consequently, the non-leaf structure of the learned tree may deviate from the hierarchical organization preferred by ground-truth labels. To address this limitation, we propose a semi-supervised hyperbolic hierarchical clustering method with set-level structural priors. The main contribution is to introduce sets as basic modeling units for hierarchy learning. Each set denotes samples expected to cohere within a subtree and is induced from leaf-level supervision together with a learned constraint-consistent similarity structure. These sets act as soft structural priors for subtree-level supervision, allowing supervision to guide non-leaf hierarchy formation beyond local leaf-level relations. Specifically, we first learn constraint-consistent embeddings to obtain a reliable set partition, then construct constraint-induced sets and estimate inter-set similarities to form set-level structural priors. Finally, these priors are incorporated into a hyperbolic hierarchy objective for continuous tree optimization. Experiments on eleven benchmark datasets and ablation studies show that the proposed method consistently improves label consistency over representative hierarchical clustering baselines while also enhancing similarity-based tree quality.

Problem

• semi-supervised hierarchical clustering
• structural priors
• subtree coherence
• leaf-level supervision
• hierarchical organization

Innovation

• semi-supervised hierarchical clustering
• set-level structural priors
• hyperbolic embedding
• constraint-consistent similarity
• subtree coherence