Home > Publications database > FairDen: Fair Density-Based Clustering > print

001     1041725
005     20251217202223.0
037 _ _ |a FZJ-2025-02405
041 _ _ |a English
100 1 _ |a Krieger, Lena
|0 P:(DE-Juel1)196726
|b 0
|e Corresponding author
|u fzj
111 2 _ |a The Thirteenth International Conference on Learning Representations
|g ICLR2025
|c Singapore
|d 2025-04-24 - 2025-04-28
|w Singapore
245 _ _ |a FairDen: Fair Density-Based Clustering
260 _ _ |c 2025
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a CONFERENCE_POSTER
|2 ORCID
336 7 _ |a Output Types/Conference Poster
|2 DataCite
336 7 _ |a Poster
|b poster
|m poster
|0 PUB:(DE-HGF)24
|s 1765959282_22744
|2 PUB:(DE-HGF)
|x Invited
520 _ _ |a 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.
536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5112
|c POF4-511
|f POF IV
|x 0
700 1 _ |a Beer, Anna
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Matthews, Pernille
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Thiesson, Anneka
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Assent, Ira
|0 P:(DE-Juel1)188313
|b 4
|u fzj
856 4 _ |u https://iclr.cc/virtual/2025/poster/29171
856 4 _ |u https://juser.fz-juelich.de/record/1041725/files/Poster.png
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/1041725/files/Poster.gif?subformat=icon
|x icon
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/1041725/files/Poster.jpg?subformat=icon-1440
|x icon-1440
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/1041725/files/Poster.jpg?subformat=icon-180
|x icon-180
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/1041725/files/Poster.jpg?subformat=icon-640
|x icon-640
|y Restricted
909 C O |o oai:juser.fz-juelich.de:1041725
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)196726
910 1 _ |a Ludwig-Maximilians-Universität München
|0 I:(DE-HGF)0
|b 0
|6 P:(DE-Juel1)196726
910 1 _ |a Universität Wien
|0 I:(DE-HGF)0
|b 1
|6 P:(DE-HGF)0
910 1 _ |a Aarhus University
|0 I:(DE-HGF)0
|b 2
|6 P:(DE-HGF)0
910 1 _ |a Aarhus University
|0 I:(DE-HGF)0
|b 3
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)188313
910 1 _ |a Aarhus University
|0 I:(DE-HGF)0
|b 4
|6 P:(DE-Juel1)188313
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5112
|x 0
914 1 _ |y 2025
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IAS-8-20210421
|k IAS-8
|l Datenanalyse und Maschinenlernen
|x 0
980 _ _ |a poster
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IAS-8-20210421
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21