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CODAC: Constraint-based Deep Active Clustering
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DOI：10.1007/s10618-026-01229-4.png)
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Abstract
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Constraint-based Deep Active Clustering (CODAC) integrates actively selected pairwise constraints into deep representation learning to efficiently improve existing cluster structures, even under tight query budgets. CODAC encodes the constraint information into the embedding so that the learned representation can generalize to unconstrained data, leading to a rapid improvement of the clustering quality even on large datasets. CODAC makes minimal assumptions regarding the data and can be combined with a wide variety of deep clustering models. It does not require the number of clusters to be known a priori and is even effective if the initial estimate is badly misspecified. Across diverse image, text, and tabular datasets, CODAC consistently attains higher cluster quality with fewer queries than the previous state-of-the-art, and can substantially improve the clustering quality with just 100–200 queries compared to the deep clustering baselines.
Keywords:
Active clustering
Deep clustering
Constrained clustering
Representation learning
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