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Debarati Das

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AAMAS Conference 2026 Conference Paper

A Generic Framework for Fair Consensus Clustering in Streams

Diptarka Chakraborty
Kushagra Chatterjee
Debarati Das
Tien-Long Nguyen

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, intoasinglepartitioningthatbestreflectstheoverall structure of the underlying dataset. Recent work by Chakraborty et al. [COLT’25] 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 prohibitivelyexpensive 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 andmemoryislimited. Wedesignthefirstconstant-factoralgorithm thatprocessesthestreamwhilestoringonlyalogarithmicnumberof inputs. Enroute, weintroduceanewgenericalgorithmicframework that integrates closest fair clustering with cluster fitting, yielding improved approximation guarantees not only in the streaming settingbutalsowhenrevisitedoffline. Furthermore, theframework is fairness-agnostic: it applies to any fairness definition for which anapproximatelyclosefairclusteringcanbecomputedefficiently. Finally, we extend our methods to the more general 𝑘-median consensusclustering problem.

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AAAI Conference 2026 Conference Paper

Generalizing Fair Clustering to Multiple Groups: Algorithms and Applications

Diptarka Chakraborty
Kushagra Chatterjee
Debarati Das
Tien-Long Nguyen

Clustering is a fundamental task in machine learning and data analysis, but it frequently fails to provide fair representation for various marginalized communities defined by multiple protected attributes -- a shortcoming often caused by biases in the training data. As a result, there is a growing need to enhance the fairness of clustering outcomes, ideally by making minimal modifications, possibly as a post-processing step after conventional clustering. A recent work initiated the study of closest fair clustering, though in a restricted scenario where data points belong to only two groups. In practice, however, data points are typically characterized by many groups, reflecting diverse protected attributes such as age, ethnicity, gender, etc. In this work, we generalize the study of the closest fair clustering problem to settings with an arbitrary number (more than two) of groups. We begin by showing that the problem is NP-hard even when all groups are of equal size -- a stark contrast with the two-group case, for which an exact algorithm exists. Next, we propose near-linear time approximation algorithms that efficiently handle arbitrary-sized multiple groups. Leveraging our closest fair clustering algorithms, we further achieve improved approximation guarantees for the fair correlation clustering problem, advancing the state-of-the-art results. Additionally, we are the first to provide approximation algorithms for the fair consensus clustering problem involving multiple (more than two) groups.

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