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Center-based clustering (e.g., $k$-means, $k$-medians) and clustering usinglinear subspaces are two most popular techniques to partition real-world datainto smaller clusters. However, when the data consists of sensitive demographicgroups, significantly different clustering cost per point for differentsensitive groups can lead to fairness-related harms (e.g., differentquality-of-service). The goal of socially fair clustering is to minimize themaximum cost of clustering per point over all groups. In this work, we proposea unified framework to solve socially fair center-based clustering and linearsubspace clustering, and give practical, efficient approximation algorithms forthese problems. We do extensive experiments to show that on multiple benchmarkdatasets our algorithms either closely match or outperform state-of-the-artbaselines.

Socially Fair Center-based and Linear Subspace Clustering