# A Generic Framework for Fair Consensus Clustering in Streams

arXiv: https://arxiv.org/abs/2602.11500 · HTML: https://mlsanity.com/paper/2602.11500/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Coresets for Clustering with Fairness Constraints](https://mlsanity.com/paper/1906.08484/) (2019)
- [On the cost of essentially fair clusterings](https://mlsanity.com/paper/1811.10319/) (2018)
- [Fair Correlation Clustering](https://mlsanity.com/paper/2002.02274/) (2020)
- [One-Shot Coresets: The Case of k-Clustering](https://mlsanity.com/paper/1711.09649/) (2017)
- [Coresets for Clustering in Excluded-minor Graphs and Beyond](https://mlsanity.com/paper/2004.07718/) (2020)
- [Improved Approximation for Fair Correlation Clustering](https://mlsanity.com/paper/2206.05050/) (2022)
- [Understanding the Cluster LP for Correlation Clustering](https://mlsanity.com/paper/2404.17509/) (2024)
- [Clustering Permutations: New Techniques with Streaming Applications](https://mlsanity.com/paper/2212.01821/) (2022)
- [Improved Rank Aggregation under Fairness Constraint](https://mlsanity.com/paper/2505.10006/) (2025)
- [Towards Fair Representation: Clustering and Consensus](https://mlsanity.com/paper/2506.08673/) (2025)

## Abstract (arXiv)

Consensus clustering seeks to combine multiple clusterings of the same dataset, potentially derived by considering various non-sensitive attributes by different agents in a multi-agent environment, into a single partitioning that best reflects the overall structure of the underlying dataset. Recent work by Chakraborty et al, introduced a fair variant under proportionate fairness and obtained a constant-factor approximation by naively selecting the best closest fair input clustering; however, their offline approach requires storing all input clusterings, which is prohibitively expensive for most large-scale applications. In this paper, we initiate the study of fair consensus clustering in the streaming model, where input clusterings arrive sequentially and memory is limited. We design the first constant-factor algorithm that processes the stream while storing only a logarithmic number of inputs. En route, we introduce a new generic algorithmic framework that integrates closest fair clustering with cluster fitting, yielding improved approximation guarantees not only in the streaming setting but also when revisited offline. Furthermore, the framework is fairness-agnostic: it applies to any fairness definition for which an approximately close fair clustering can be computed efficiently. Finally, we extend our methods to the more general k-median consensus clustering problem.