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001041724 005__ 20251217202223.0
001041724 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-02404
001041724 037__ $$aFZJ-2025-02404
001041724 041__ $$aEnglish
001041724 1001_ $$0P:(DE-Juel1)196726$$aKrieger, Lena$$b0$$eCorresponding author$$ufzj
001041724 1112_ $$aThe Thirteenth International Conference on Learning Representations$$cSingapore$$d2025-04-24 - 2025-04-28$$gICLR2025$$wSingapore
001041724 245__ $$aFairDen: Fair Density-Based Clustering
001041724 260__ $$c2025
001041724 300__ $$a-
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001041724 520__ $$aFairness 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.
001041724 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001041724 7001_ $$0P:(DE-HGF)0$$aBeer, Anna$$b1
001041724 7001_ $$0P:(DE-HGF)0$$aMatthews, Pernille$$b2
001041724 7001_ $$0P:(DE-HGF)0$$aThiesson, Anneka$$b3
001041724 7001_ $$0P:(DE-Juel1)188313$$aAssent, Ira$$b4$$eLast author$$ufzj
001041724 8564_ $$uhttps://juser.fz-juelich.de/record/1041724/files/FairDen-FullPaper.pdf$$yOpenAccess
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001041724 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Universität Wien$$b1
001041724 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aAarhus University$$b2
001041724 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Aarhus University$$b3
001041724 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188313$$aForschungszentrum Jülich$$b4$$kFZJ
001041724 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)188313$$a Aarhus University$$b4
001041724 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001041724 9141_ $$y2025
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