改进K均值聚类的公平性，避免某类群体被过度集中或忽略。
A Computational Approach to Improving Fairness in K-means Clustering
- 先聚类再调整少数关键点归属，提升公平性。
- 实验显示公平性显著提升，聚类质量影响极小。
- 方法通用性强，适用于多种聚类算法和公平度量。
K均值聚类算法在后续分析或解释中可能存在重大缺陷：某些聚类在敏感变量（如性别或种族）上可能包含过多（或过少）的子群体样本，这种不公平问题可能导致偏见及意外社会后果。本文提出一种两阶段优化框架——先进行聚类，再调整一小部分选定数据点的簇归属。设计了两种计算高效的算法识别对公平性影响大的数据点：一种聚焦于簇外最近的数据点，另一种关注高度‘混合’的数据点。在基准数据集上的实验表明，该方法能显著提升公平性，同时对聚类质量的影响微乎其微。所提算法可轻松扩展至一类广泛的聚类算法或公平性度量。
原文摘要 · Abstract (English)
The popular K-means clustering algorithm potentially suffers from a major weakness for further analysis or interpretation. Some cluster may have disproportionately more (or fewer) points from one of the subpopulations in terms of some sensitive variable, e.g., gender or race. Such a fairness issue may cause bias and unexpected social consequences. This work attempts to improve the fairness of K-means clustering with a two-stage optimization formulation--clustering first and then adjust cluster membership of a small subset of selected data points. Two computationally efficient algorithms are proposed in identifying those data points that are expensive for fairness, with one focusing on nearest data points outside of a cluster and the other on highly 'mixed' data points. Experiments on benchmark datasets show substantial improvement on fairness with a minimal impact to clustering quality. The proposed algorithms can be easily extended to a broad class of clustering algorithms or fairness metrics.
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