001041725 001__ 1041725
001041725 005__ 20251217202223.0
001041725 037__ $$aFZJ-2025-02405
001041725 041__ $$aEnglish
001041725 1001_ $$0P:(DE-Juel1)196726$$aKrieger, Lena$$b0$$eCorresponding author$$ufzj
001041725 1112_ $$aThe Thirteenth International Conference on Learning Representations$$cSingapore$$d2025-04-24 - 2025-04-28$$gICLR2025$$wSingapore
001041725 245__ $$aFairDen: Fair Density-Based Clustering
001041725 260__ $$c2025
001041725 3367_ $$033$$2EndNote$$aConference Paper
001041725 3367_ $$2BibTeX$$aINPROCEEDINGS
001041725 3367_ $$2DRIVER$$aconferenceObject
001041725 3367_ $$2ORCID$$aCONFERENCE_POSTER
001041725 3367_ $$2DataCite$$aOutput Types/Conference Poster
001041725 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1765959282_22744$$xInvited
001041725 520__ $$aFairness in data mining tasks like clustering has recently become an increasinglyimportant aspect. However, few clustering algorithms exist that focus on fairgroupings of data with sensitive attributes. Including fairness in the clusteringobjective is especially hard for density-based clustering, as it does not directlyoptimize a closed form objective like centroid-based or spectral methods.This paper introduces FairDen, the first fair, density-based clustering algorithm.We capture the dataset’s density-connectivity structure in a similarity matrix thatwe manipulate to encourage a balanced clustering. In contrast to state-of-theart, FairDen inherently handles categorical attributes, noise, and data with severalsensitive attributes or groups. We show that FairDen finds meaningful and fairclusters in extensive experiments.
001041725 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001041725 7001_ $$0P:(DE-HGF)0$$aBeer, Anna$$b1
001041725 7001_ $$0P:(DE-HGF)0$$aMatthews, Pernille$$b2
001041725 7001_ $$0P:(DE-HGF)0$$aThiesson, Anneka$$b3
001041725 7001_ $$0P:(DE-Juel1)188313$$aAssent, Ira$$b4$$ufzj
001041725 8564_ $$uhttps://iclr.cc/virtual/2025/poster/29171
001041725 8564_ $$uhttps://juser.fz-juelich.de/record/1041725/files/Poster.png$$yRestricted
001041725 8564_ $$uhttps://juser.fz-juelich.de/record/1041725/files/Poster.gif?subformat=icon$$xicon$$yRestricted
001041725 8564_ $$uhttps://juser.fz-juelich.de/record/1041725/files/Poster.jpg?subformat=icon-1440$$xicon-1440$$yRestricted
001041725 8564_ $$uhttps://juser.fz-juelich.de/record/1041725/files/Poster.jpg?subformat=icon-180$$xicon-180$$yRestricted
001041725 8564_ $$uhttps://juser.fz-juelich.de/record/1041725/files/Poster.jpg?subformat=icon-640$$xicon-640$$yRestricted
001041725 909CO $$ooai:juser.fz-juelich.de:1041725$$pVDB
001041725 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)196726$$aForschungszentrum Jülich$$b0$$kFZJ
001041725 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)196726$$a Ludwig-Maximilians-Universität München$$b0
001041725 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Universität Wien$$b1
001041725 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Aarhus University$$b2
001041725 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Aarhus University$$b3
001041725 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188313$$aForschungszentrum Jülich$$b4$$kFZJ
001041725 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)188313$$a Aarhus University$$b4
001041725 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$$x0
001041725 9141_ $$y2025
001041725 920__ $$lyes
001041725 9201_ $$0I:(DE-Juel1)IAS-8-20210421$$kIAS-8$$lDatenanalyse und Maschinenlernen$$x0
001041725 980__ $$aposter
001041725 980__ $$aVDB
001041725 980__ $$aI:(DE-Juel1)IAS-8-20210421
001041725 980__ $$aUNRESTRICTED