Active correlation clustering leverages cost-efficient learning to manage pairwise similarities, suggesting a new approach to cold-start scenarios.
We study active correlation clustering where pairwise similarities are not provided upfront and must be queried in a cost-efficient manner through active learning. Specifically, we focus on the cold-start scenario, where no true initial pairwise similarities are available for active learning. To address this challenge, we propose a coverage-aware method that encourages diversity early in the process. We demonstrate the effectiveness of our approach through several synthetic and real-world experiments.
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Aronsson et al. (2025) studied this question.
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