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Parameterized Approximation Schemes for Fair Clustering in Doubling Metrics
This paper focuses on the k-center problem in bounded doubling metrics under two popular fairness requirements: group fairness and data summarization fairness, referred to Group Fair k-Center (Gf-k-Cen) and Data Summarization Fair k-Center (Dsf-k-Cen), respectively.
A Single-Swap Local Search Algorithm for k-Means of Lines
A proportional capture relation is introduced that links optimal and current centers based the assignment proportions of lines, enabling a refined analysis that bypasses the triangle inequality barrier.