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

ArXi:2606.01525v1 Announce Type: new 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.