论文标题
集群级群体代表性公平性$ k $ - ameans群集
Cluster-level Group Representativity Fairness in $k$-means Clustering
论文作者
论文摘要
最近，人们对开发公平的聚类算法有很大的兴趣，这些算法试图对沿敏感属性（例如种族和性别）定义的群体的代表伸张正义。我们观察到聚类算法可以生成簇，使不同的组在不同的簇中处于不利地位。我们开发了一种聚类算法，基于经典算法（例如$ k $ - 英镑）所启用的质心聚类范式，我们致力于减轻每个集群中最不分散的群体所经历的不公平性。我们的方法使用迭代优化范式，从而通过将对象重新分配到簇中，从而修改了初始群集分配，从而使每个集群中最坏的敏感组受益。我们通过对现实世界数据集的新评估度量的广泛经验评估来证明我们的方法的有效性。具体而言，我们表明我们的方法有效地增强了群集级的代表性公平，在对群集相干性的影响下显着。
There has been much interest recently in developing fair clustering algorithms that seek to do justice to the representation of groups defined along sensitive attributes such as race and gender. We observe that clustering algorithms could generate clusters such that different groups are disadvantaged within different clusters. We develop a clustering algorithm, building upon the centroid clustering paradigm pioneered by classical algorithms such as $k$-means, where we focus on mitigating the unfairness experienced by the most-disadvantaged group within each cluster. Our method uses an iterative optimisation paradigm whereby an initial cluster assignment is modified by reassigning objects to clusters such that the worst-off sensitive group within each cluster is benefitted. We demonstrate the effectiveness of our method through extensive empirical evaluations over a novel evaluation metric on real-world datasets. Specifically, we show that our method is effective in enhancing cluster-level group representativity fairness significantly at low impact on cluster coherence.
