k-Clustering with Fair Outliers
Matteo Almanza, Alessandro Epasto, Alessandro Panconesi, Giuseppe Del Re · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining · 2022
Clustering problems and clustering algorithms are often overly sensitive to the presence of outliers: even a handful of points can greatly affect the structure of the optimal solution and its cost. This is why many algorithms for robust clustering problems have been formulated in recent years. These algorithms discard some points as outliers, excluding them from the clustering. However, outlier selection can be unfair: some categories of input points may be disproportionately affected by the outlier removal algorithm.