%0 Conference Paper
%A Krieger, Lena
%A Beer, Anna
%A Matthews, Pernille
%A Thiesson, Anneka
%A Assent, Ira
%T FairDen: Fair Density-Based Clustering
%M FZJ-2025-02405
%D 2025
%X Fairness in data mining tasks like clustering has recently become an increasinglyimportant aspect. However, few clustering algorithms exist that focus on fairgroupings of data with sensitive attributes. Including fairness in the clusteringobjective is especially hard for density-based clustering, as it does not directlyoptimize a closed form objective like centroid-based or spectral methods.This paper introduces FairDen, the first fair, density-based clustering algorithm.We capture the dataset’s density-connectivity structure in a similarity matrix thatwe manipulate to encourage a balanced clustering. In contrast to state-of-theart, FairDen inherently handles categorical attributes, noise, and data with severalsensitive attributes or groups. We show that FairDen finds meaningful and fairclusters in extensive experiments.
%B The Thirteenth International Conference on Learning Representations
%C 24 Apr 2025 - 28 Apr 2025, Singapore (Singapore)
Y2 24 Apr 2025 - 28 Apr 2025
M2 Singapore, Singapore
%F PUB:(DE-HGF)24
%9 Poster
%U https://juser.fz-juelich.de/record/1041725