Fair, Polylog-Approximate Low-Cost Hierarchical Clustering

Marina Knittel, Max Springer, John P Dickerson, MohammadTaghi Hajiaghayi

Keywords: Fair machine learning, hierarchical clustering, clustering

TL;DR: We propose the first polylog-approximate fair hierarchical clustering algorithm optimizing for Dasgupta's cost function.

Abstract: Research in fair machine learning, and particularly clustering, has been crucial in recent years given the many ethical controversies that modern intelligent systems have posed. Ahmadian et al. [2020] established the study of fairness in hierarchical clustering, a stronger, more structured variant of its well-known flat counterpart, though their proposed algorithm that optimizes for Dasgupta's [2016] famous cost function was highly theoretical. Knittel et al. [2023] then proposed the first practical fair approximation for cost, however they were unable to break the polynomial-approximate barrier they posed as a hurdle of interest. We break this barrier, proposing the first truly polylogarithmic-approximate low-cost fair hierarchical clustering, thus greatly bridging the gap between the best fair and vanilla hierarchical clustering approximations.

Submission Number: 15086