Proportionally Representative Clustering

H. Aziz, B. Lee, S. Morota Chu, and J. Vollen. Proportionally Representative Clustering. WINE 2024.

 [ Pdf ] 


In recent years, there has been a surge in effort to formalize notions of fairness in machine learning. We focus on centroid clustering— one of the fundamental tasks in unsupervised machine learning. We pro- pose a new axiom “proportionally representative fairness” (PRF) that is designed for clustering problems where the selection of centroids re- flects the distribution of data points and how tightly they are clustered together. Our fairness concept is not satisfied by existing fair cluster- ing algorithms. We design efficient algorithms to achieve PRF both for unconstrained and discrete clustering problems. Our algorithm for the unconstrained setting is also the first known polynomial-time approxi- mation algorithm for the well-studied Proportional Fairness (PF) axiom [13]. Our algorithm for the discrete setting also matches the best known approximation factor for PF.

 


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