Fair Minimum Representation Clustering via Integer Programming

• URL: http://arxiv.org/abs/2409.02963v1
• Date: Wed, 4 Sep 2024 00:13:40 GMT
• Title: Fair Minimum Representation Clustering via Integer Programming
• Authors: Connor Lawless, Oktay Gunluk,
• Abstract summary: Clustering is an unsupervised learning task that aims to partition data into a set of clusters. In this paper, we study the k-means and k-medians clustering problems with the additional constraint that each group must have a minimum level of representation. We present an alternating minimization algorithm, called MiniReL, that directly incorporates the fairness constraints.
• Score: 0.6906005491572401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clustering is an unsupervised learning task that aims to partition data into a set of clusters. In many applications, these clusters correspond to real-world constructs (e.g., electoral districts, playlists, TV channels) whose benefit can only be attained by groups when they reach a minimum level of representation (e.g., 50\% to elect their desired candidate). In this paper, we study the k-means and k-medians clustering problems with the additional constraint that each group (e.g., demographic group) must have a minimum level of representation in at least a given number of clusters. We formulate the problem through a mixed-integer optimization framework and present an alternating minimization algorithm, called MiniReL, that directly incorporates the fairness constraints. While incorporating the fairness criteria leads to an NP-Hard assignment problem within the algorithm, we provide computational approaches that make the algorithm practical even for large datasets. Numerical results show that the approach is able to create fairer clusters with practically no increase in the clustering cost across standard benchmark datasets.

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