A Computational Approach to Improving Fairness in K-means Clustering

This research addresses fairness issues in K-means clustering, where some clusters may disproportionately represent certain subpopulations, leading to bias. It proposes a two-stage optimization approach and two efficient algorithms to identify and adjust problematic data points, improving fairness with minimal impact on clustering quality.