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As image collections become ever larger, effective access to their content requires a meaningful categorization of the images. Such a categorization can rely on clustering methods working on image features, but should greatly benefit from any form of supervision the user can provide, related to the visual content. Semi-supervised clustering - learning from both labelled and unlabelled data - has consequently become a topic of significant interest. In this paper we present a new semi-supervised clustering algorithm, pairwise-constrained competitive agglomeration, which is based on a fuzzy cost function that takes pairwise constraints into account.