Group Fair Clustering Revisited -- Notions and Efficient Algorithm

This paper considers the problem of group fairness in clustering. We propose a new fairness notion which strictly generalizes existing notions, and we theoretically analyze the relationships between several existing notions. Finally, we propose a simple and efficient greedy round-robin-based algorithm (FRAC-OE) and extensive experiments to validate its efficacy across multiple datasets.