以群体福祉为核心，重新定义公平聚类的优化目标。
Welfare-Centric Clustering
- 基于距离与比例代表性构建群体效用模型。
- 提出罗尔斯式与功利主义两类优化目标，性能显著优于现有方法。
- 适合关注公平性与群体利益平衡的研究者和实践者。
公平聚类传统上关注群体代表性的均等或各组聚类成本的均衡。然而，Dickerson 等人（2025）指出，这些公平性定义可能导致不理想或反直觉的聚类结果，并倡导以福祉为核心的聚类方法，通过建模各群体效用实现优化。本文基于距离与比例代表性共同建模群体效用，提出了两种基于福祉的优化目标：罗尔斯式（平等主义）与功利主义目标。我们设计了针对这两类目标的新算法，并证明了其理论保证。在多个真实世界数据集上的实验表明，本方法显著优于现有的公平聚类基线。
原文摘要 · Abstract (English)
Fair clustering has traditionally focused on ensuring equitable group representation or equalizing group-specific clustering costs. However, Dickerson et al. (2025) recently showed that these fairness notions may yield undesirable or unintuitive clustering outcomes and advocated for a welfare-centric clustering approach that models the utilities of the groups. In this work, we model group utilities based on both distances and proportional representation and formalize two optimization objectives based on welfare-centric clustering: the Rawlsian (Egalitarian) objective and the Utilitarian objective. We introduce novel algorithms for both objectives and prove theoretical guarantees for them. Empirical evaluations on multiple real-world datasets demonstrate that our methods significantly outperform existing fair clustering baselines.
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