Socially Fair Center-based and Linear Subspace Clustering

TL;DR

This paper introduces a unified framework and efficient algorithms for socially fair clustering, aiming to reduce demographic disparities in clustering costs across sensitive groups.

Contribution

It presents the first unified approach with practical algorithms for socially fair center-based and linear subspace clustering, addressing fairness in data partitioning.

Findings

Algorithms closely match or outperform baselines on benchmark datasets.

Proposed methods effectively reduce maximum group clustering costs.

Framework is applicable to multiple clustering techniques.

Abstract

Center-based clustering (e.g., $k$-means, $k$-medians) and clustering using linear subspaces are two most popular techniques to partition real-world data into smaller clusters. However, when the data consists of sensitive demographic groups, significantly different clustering cost per point for different sensitive groups can lead to fairness-related harms (e.g., different quality-of-service). The goal of socially fair clustering is to minimize the maximum cost of clustering per point over all groups. In this work, we propose a unified framework to solve socially fair center-based clustering and linear subspace clustering, and give practical, efficient approximation algorithms for these problems. We do extensive experiments to show that on multiple benchmark datasets our algorithms either closely match or outperform state-of-the-art baselines.