Proportionally Fair Clustering

9 May 2019 Xingyu Chen Brandon Fain Liang Lyu Kamesh Munagala

We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context. For clustering $n$ points with $k$ centers, we define fairness as proportionality to mean that any $n/k$ points are entitled to form their own cluster if there is another center that is closer in distance for all $n/k$ points... (read more)

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