Maity, BinitaBinitaMaityDas, ShrutimoyShrutimoyDasDasgupta, AnirbanAnirbanDasgupta2026-03-182026-03-182025-11-1210.1109/ICDMW69685.2025.00045https://repository.iitgn.ac.in/handle/IITG2025/34887Individual fairness guarantees are often desirable properties to have, but they become hard to formalize when the dataset contains outliers. Here, we investigate the problem of developing an individually fair k-median and k-means clustering algorithm for datasets that contain outliers. That is, given n points and k centers, we want that for each point which is not an outlier, there must be a center within the nk nearest neighbours of the given point. While a few of the recent works have looked into individually fair clustering, this is the first work that explores this problem in the presence of outliers for k-clustering. For this purpose, we define and solve a linear program (LP) that helps us identify the outliers. We exclude these outliers from the dataset and apply a rounding algorithm that computes the k centers, such that the fairness constraint of the remaining points is satisfied. We also provide theoretical guarantees that our method leads to a guaranteed approximation of the fair radius as well as the clustering cost. We also demonstrate our techniques empirically on real-world datasets.en-USIndividually fair clusteringOutliersConference Paper