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Fair Model-based Clustering

arXiv 2602.21509 ↗First posted 25 Feb 2026stat.ML cs.LG stat.APcited by 0

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Abstract (arXiv)

The goal of fair clustering is to find clusters such that the proportion of sensitive attributes (e.g., gender, race, etc.) in each cluster is similar to that of the entire dataset. Various fair clustering algorithms have been proposed that modify standard K-means clustering to satisfy a given fairness constraint. A critical limitation of several existing fair clustering algorithms is that the number of parameters to be learned is proportional to the sample size because the cluster assignment of each datum should be optimized simultaneously with the cluster center, and thus scaling up the algorithms is difficult. In this paper, we propose a new fair clustering algorithm based on a finite mixture model, called Fair Model-based Clustering (FMC). A main advantage of FMC is that the number of learnable parameters is independent of the sample size and thus can be scaled up easily. In particular, mini-batch learning is possible to obtain clusters that are approximately fair. Moreover, FMC can be applied to non-metric data (e.g., categorical data) as long as the likelihood is well-defined. Theoretical and empirical justifications for the superiority of the proposed algorithm are provided.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 25 Feb 2026.

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References · 21

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  3. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  4. 2018Fair Clustering Through Fairletscited by 176
  5. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  6. 2020Algorithmic Fairnesscited by 143
  7. 2019Fair Algorithms for Clusteringcited by 98
  8. 2019Scalable Fair Clusteringcited by 78
  9. 2019Guarantees for Spectral Clustering with Fairness Constraintscited by 72
  10. 2023ClusterLLM: Large Language Models as a Guide for Text Clusteringcited by 70
  11. 2020Learning Meta Face Recognition in Unseen Domainscited by 61
  12. 2021RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHRcited by 32
9 more
  1. 2021Fair Representation Learning for Heterogeneous Information Networkscited by 28
  2. 2021Fair Clustering Under a Bounded Costcited by 21
  3. 2015Mixture models with a prior on the number of componentscited by 20
  4. 2017An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimizationcited by 20
  5. 2022Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metriccited by 17
  6. 2019Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Datacited by 13
  7. 2020Learning Smooth and Fair Representationscited by 12
  8. 2020KFC: A Scalable Approximation Algorithm for k-center Fair Clusteringcited by 12
  9. 2017From Image to Text Classification: A Novel Approach based on Clustering Word Embeddingscited by 6

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