Angular Constraint Embedding via SpherePair Loss for Constrained Clustering
Shaojie Zhang, Ke Chen
Constrained clustering integrates domain knowledge through pairwise constraints.However, existing deep constrained clustering (DCC) methods are either limited by anchors inherent in end-to-end modeling or struggle with learning discriminative Euclidean embedding, restricting their scalability and real-world applicability.To avoid their respective pitfalls, we propose a novel angular constraint embedding approach for DCC, termed SpherePair.Using the SpherePair loss with a geometric formulation,our method faithfully encodes pairwise constraints and leads to embeddings that are clustering-friendly in angular space, effectively separating representation learning from clustering. SpherePair preserves pairwise relations without conflicts,requires no exact cluster number for constraint embedding, generalizes to unseen data, and is supported by rigorous theoretical guarantees.Comparative evaluations with state-of-the-art DCC methods on diverse benchmarks, along with empirical validation of theoretical insights, confirm its superior performance, scalability, and overall real-world effectiveness.