We introduce the fair-capacitated clustering problem that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing cluster cardinalities. We propose a two-step solution to the problem: i) we rely on fairlets to generate minimal sets that satisfy the fair constraint and ii) we propose two approaches: hierarchical clustering and partitioning-based clustering, to obtain the fair-capacitated clustering.

Tai Le Quy, Arjun Roy, Gunnar Friege and Eirini Ntoutsi “Fair-Capacitated Clustering”. 2021. In: Proceedings of The 14th International Conference on Educational Data Mining (EDM21). International Educational Data Mining Society, 407-414. https://educationaldatamining.org/edm2021/ EDM ’21 June 29 - July 02 2021, Paris, France