TY  - CONF
AU  - Krieger, Lena
AU  - Beer, Anna
AU  - Matthews, Pernille
AU  - Thiesson, Anneka
AU  - Assent, Ira
TI  - FairDen: Fair Density-Based Clustering
M1  - FZJ-2025-02404
SP  - -
PY  - 2025
AB  - Fairness in data mining tasks like clustering has recently become an increasingly important aspect. However, few clustering algorithms exist that focus on fair groupings of data with sensitive attributes. Including fairness in the clustering objective is especially hard for density-based clustering, as it does not directly optimize 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 that we manipulate to encourage a balanced clustering. In contrast to state-of-the-art, FairDen inherently handles categorical attributes, noise, and data with several sensitive attributes or groups.We show that FairDen finds meaningful and fair clusters in extensive experiments.
T2  - The Thirteenth International Conference on Learning Representations
CY  - 24 Apr 2025 - 28 Apr 2025, Singapore (Singapore)
Y2  - 24 Apr 2025 - 28 Apr 2025
M2  - Singapore, Singapore
LB  - PUB:(DE-HGF)8
DO  - DOI:10.34734/FZJ-2025-02404
UR  - https://juser.fz-juelich.de/record/1041724
ER  -