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@INPROCEEDINGS{Krieger:1041724,
      author       = {Krieger, Lena and Beer, Anna and Matthews, Pernille and
                      Thiesson, Anneka and Assent, Ira},
      title        = {{F}air{D}en: {F}air {D}ensity-{B}ased {C}lustering},
      reportid     = {FZJ-2025-02404},
      pages        = {-},
      year         = {2025},
      abstract     = {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.},
      month         = {Apr},
      date          = {2025-04-24},
      organization  = {The Thirteenth International
                       Conference on Learning Representations,
                       Singapore (Singapore), 24 Apr 2025 - 28
                       Apr 2025},
      cin          = {IAS-8},
      cid          = {I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.34734/FZJ-2025-02404},
      url          = {https://juser.fz-juelich.de/record/1041724},
}