A General Anchor-Based Framework for Scalable Fair Clustering

2025-11-13

AI Summary

Existing fair clustering algorithms typically exhibit quadratic or higher time complexity, rendering them infeasible for large-scale datasets. Method: We propose a generic, plug-and-play anchor-based fair clustering framework that enforces global fairness via joint group-label constraints; introduces efficient fair sampling and anchor graph construction to reduce computational complexity to linear time; and integrates an ADMM-based optimization solver with an optimization-driven label propagation mechanism, enabling linear scalability for arbitrary baseline fair clustering algorithms. Contribution/Results: Evaluated on multiple large-scale benchmarks, our method achieves 10×–1000× speedup over state-of-the-art approaches while maintaining competitive clustering quality and provable fairness guarantees.

Technology Category

• Machine Learning: Clustering
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Fair clustering is crucial for mitigating bias in unsupervised learning, yet existing algorithms often suffer from quadratic or super-quadratic computational complexity, rendering them impractical for large-scale datasets. To bridge this gap, we introduce the Anchor-based Fair Clustering Framework (AFCF), a novel, general, and plug-and-play framework that empowers arbitrary fair clustering algorithms with linear-time scalability. Our approach first selects a small but representative set of anchors using a novel fair sampling strategy. Then, any off-the-shelf fair clustering algorithm can be applied to this small anchor set. The core of our framework lies in a novel anchor graph construction module, where we formulate an optimization problem to propagate labels while preserving fairness. This is achieved through a carefully designed group-label joint constraint, which we prove theoretically ensures that the fairness of the final clustering on the entire dataset matches that of the anchor clustering. We solve this optimization efficiently using an ADMM-based algorithm. Extensive experiments on multiple large-scale benchmarks demonstrate that AFCF drastically accelerates state-of-the-art methods, which reduces computational time by orders of magnitude while maintaining strong clustering performance and fairness guarantees.

Problem

• Mitigating bias in unsupervised learning through fair clustering
• Reducing computational complexity of fair clustering algorithms
• Enabling linear-time scalability for large-scale datasets

Innovation

• Anchor-based framework enables linear-time fair clustering
• Fair sampling selects representative anchors for scalability
• ADMM optimization propagates labels with fairness guarantees