无需调参的聚类新方法，自动确定最优簇数。
Parameter-Free Clustering via Self-Supervised Consensus Maximization (Extended Version)
- 通过自监督学习构建结构感知表示，动态评估聚类合并决策。
- 以邻居共识度最大时刻作为最优簇数判断依据，效果优于现有方法。
- 适合真实场景中未知簇数的聚类任务，无需人工调参。
聚类是无监督学习的基础任务，但大多数现有方法严重依赖超参数（如簇的数量）等敏感设置，限制了其在真实场景中的应用。为解决这一长期挑战，我们提出一种全新的、完全无需参数的聚类框架——自监督共识最大化（SCMax）。该框架将层次聚合聚类与聚类评估整合为单一过程：每一步聚合时，基于当前聚类结构引导自监督学习任务，生成新的结构感知数据表示；随后引入最近邻共识分数，衡量原始表示与自监督表示所建议的最近邻合并决策之间的一致程度。当共识度达到峰值时，即为最优簇数的判定依据。在多个数据集上的大量实验表明，该框架在未知簇数场景下显著优于现有聚类方法。
原文摘要 · Abstract (English)
Clustering is a fundamental task in unsupervised learning, but most existing methods heavily rely on hyperparameters such as the number of clusters or other sensitive settings, limiting their applicability in real-world scenarios. To address this long-standing challenge, we propose a novel and fully parameter-free clustering framework via Self-supervised Consensus Maximization, named SCMax. Our framework performs hierarchical agglomerative clustering and cluster evaluation in a single, integrated process. At each step of agglomeration, it creates a new, structure-aware data representation through a self-supervised learning task guided by the current clustering structure. We then introduce a nearest neighbor consensus score, which measures the agreement between the nearest neighbor-based merge decisions suggested by the original representation and the self-supervised one. The moment at which consensus maximization occurs can serve as a criterion for determining the optimal number of clusters. Extensive experiments on multiple datasets demonstrate that the proposed framework outperforms existing clustering approaches designed for scenarios with an unknown number of clusters.
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