UniFair: A unified fair clustering approach based on separation and compactness

• URL: http://arxiv.org/abs/2606.04777v2
• Date: Thu, 04 Jun 2026 08:05:16 GMT
• Title: UniFair: A unified fair clustering approach based on separation and compactness
• Abstract summary: We propose UniFair, a unified framework that jointly optimize separation fairness and social fairness.<n>We develop gradient-based optimization procedures for separation-fair and unified k-means objectives.<n>Experiments show that UniFair reduces both boundary-related and cost-based group disparities with only a modest increase in clustering loss.
• Score: 1.918113642696782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clustering is increasingly used to support high-impact decisions, yet standard objectives such as k-means can produce clusterings that treat demographic groups unequally. Existing fair clustering methods typically optimize a single notion of fairness and often overlook how clustering costs interact with the geometry of the induced decision boundaries. We propose UniFair, a unified framework that jointly optimizes separation fairness and social fairness. Separation fairness encourages protected groups to lie farther from the induced decision boundaries, while social fairness reduces disparities in within-cluster distortion by penalizing group-wise clustering costs. We develop gradient-based optimization procedures for separation-fair and unified k-means objectives, and extend them to deep clustering by enforcing the same criteria in the latent space of an autoencoder. Experiments on tabular and image datasets show that UniFair reduces both boundary-related and cost-based group disparities with only a modest increase in clustering loss.

Related papers

• COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering [9.978074549317652]
  We propose a new fair clustering algorithm, COVA-FC.<n>We show that COVA-FC achieves competitive cost-fairness trade-offs and improves computational efficiency over existing baselines in both subgroup and higher-order marginal settings.<n> Experiments on benchmark datasets show that COVA-FC achieves competitive cost-fairness trade-offs and improves computational efficiency over existing baselines in both subgroup and higher-order marginal settings.
  arXiv  Detail & Related papers  (2026-07-20T16:14:13Z)
• Post-hoc Provider Fairness Adaptation via Hierarchical Exposure Alignment [54.58748721533078]
  Post-hoc Fairness Adaptation (PFA) is a lightweight framework that equips a frozen recommender with a fairness adapter.<n>It learns personalized additive score adjustments from user-item embeddings to steer provider exposure toward fairness.<n>PFA achieves substantial fairness gains with negligible accuracy loss, consistently outperforming strong baselines.
  arXiv  Detail & Related papers  (2026-05-02T16:32:52Z)
• Fair Clustering via Alignment [12.12426896501947]
  Algorithmic fairness in clustering aims to balance proportions of instances assigned to each cluster with respect to a given sensitive attribute.<n>We propose a new fair clustering algorithm based on a novel decomposition of the fair $K$-means clustering objective function.
  arXiv  Detail & Related papers  (2025-05-14T04:29:09Z)
• Interaction-Aware Gaussian Weighting for Clustered Federated Learning [58.92159838586751]
  Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy.<n>We propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution.<n>Our experiments on benchmark datasets show that FedGWC outperforms existing FL algorithms in cluster quality and classification accuracy.
  arXiv  Detail & Related papers  (2025-02-05T16:33:36Z)
• Self-Supervised Graph Embedding Clustering [70.36328717683297]
  K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
  arXiv  Detail & Related papers  (2024-09-24T08:59:51Z)
• Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID [56.573905143954015]
  We propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-05-22T03:27:46Z)
• Socially Fair Center-based and Linear Subspace Clustering [8.355270405285909]
  Center-based clustering and linear subspace clustering are popular techniques to partition real-world data into smaller clusters. Different clustering cost per point for different sensitive groups can lead to fairness-related harms. We propose a unified framework to solve socially fair center-based clustering and linear subspace clustering.
  arXiv  Detail & Related papers  (2022-08-22T07:10:17Z)
• Fair Clustering Under a Bounded Cost [33.50262066253557]
  Clustering is a fundamental unsupervised learning problem where a dataset is partitioned into clusters that consist of nearby points in a metric space. A recent variant, fair clustering, associates a color with each point representing its group membership and requires that each color has (approximately) equal representation in each cluster to satisfy group fairness. We consider two fairness objectives: the group utilitarian objective and the group egalitarian objective, as well as the group leximin objective which generalizes the group egalitarian objective.
  arXiv  Detail & Related papers  (2021-06-14T08:47:36Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)
• A Pairwise Fair and Community-preserving Approach to k-Center Clustering [34.386585230600716]
  Clustering is a foundational problem in machine learning with numerous applications. We define two new types of fairness in the clustering setting, pairwise fairness and community preservation.
  arXiv  Detail & Related papers  (2020-07-14T22:32:27Z)
• Distributional Individual Fairness in Clustering [7.303841123034983]
  We introduce a framework for assigning individuals, embedded in a metric space, to probability distributions over a bounded number of cluster centers. We provide an algorithm for clustering with $p$-norm objective and individual fairness constraints with provable approximation guarantee.
  arXiv  Detail & Related papers  (2020-06-22T20:02:09Z)
• Fair Hierarchical Clustering [92.03780518164108]
  We define a notion of fairness that mitigates over-representation in traditional clustering. We show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective.
  arXiv  Detail & Related papers  (2020-06-18T01:05:11Z)
• Fair Correlation Clustering [92.15492066925977]
  We obtain approximation algorithms for correlation clustering under several important types of fairness constraints. We show that fair solutions to correlation clustering can be obtained with limited increase in cost compared to the state-of-the-art (unfair) algorithms.
  arXiv  Detail & Related papers  (2020-02-06T14:28:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.