通过球面角度约束提升聚类性能，无需预设聚类数。
Angular Constraint Embedding via SpherePair Loss for Constrained Clustering
- 用球面配对损失在角度空间编码约束关系。
- 可自动推断聚类数，支持新数据快速推理。
- 理论严谨，适用于真实场景大规模聚类。
约束聚类通过成对约束融合领域知识。现有深度约束聚类（DCC）方法或受限于端到端建模中的锚点，或难以学习判别性欧氏嵌入，影响可扩展性和实际应用。为避免上述缺陷，我们提出一种新型角度约束嵌入方法SpherePair。基于几何形式的SpherePair损失，该方法准确编码成对约束，生成角度空间中利于聚类的表示，有效分离表征学习与聚类过程。SpherePair能保持无冲突的成对关系，无需指定聚类数量，可推广至未见数据，支持快速聚类数推断，并具备严格的理论保证。在多种基准上的对比实验及理论验证均表明其在性能、可扩展性与实际效果上优于现有先进方法。代码已开源。
原文摘要 · Abstract (English)
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 conflict, removes the need to specify the exact number of clusters, generalizes to unseen data, enables rapid inference of the number of clusters, 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. Code is available at \href{https://github.com/spherepaircc/SpherePairCC/tree/main}{our repository}.
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