Fair Clustering with Clusterlets
This work addresses fairness in clustering for real-world applications, presenting an incremental improvement by simplifying the discovery of starting clusters compared to existing complex methods.
The paper tackles the problem of achieving fairness in clustering by proposing clusterlet-based fuzzy clustering algorithms that match single-class clusters to optimize fairness, showing that simple matching strategies can achieve high fairness and, with parameter tuning, high cohesion and low overlap.
Given their widespread usage in the real world, the fairness of clustering methods has become of major interest. Theoretical results on fair clustering show that fairness enjoys transitivity: given a set of small and fair clusters, a trivial centroid-based clustering algorithm yields a fair clustering. Unfortunately, discovering a suitable starting clustering can be computationally expensive, rather complex or arbitrary. In this paper, we propose a set of simple \emph{clusterlet}-based fuzzy clustering algorithms that match single-class clusters, optimizing fair clustering. Matching leverages clusterlet distance, optimizing for classic clustering objectives, while also regularizing for fairness. Empirical results show that simple matching strategies are able to achieve high fairness, and that appropriate parameter tuning allows to achieve high cohesion and low overlap.