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• W2951567704 abstract "Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering process. Typically, this supervision is provided by the user in the form of pairwise constraints. Existing methods use such constraints in one of the following ways: they adapt their clustering procedure, their similarity metric, or both. All of these approaches operate within the scope of individual clustering algorithms. In contrast, we propose to use constraints to choose between clusterings generated by very different unsupervised clustering algorithms, run with different parameter settings. We empirically show that this simple approach often outperforms existing semi-supervised clustering methods." @default.
• W2951567704 created "2019-06-27" @default.
• W2951567704 creator A5026999802 @default.
• W2951567704 creator A5086906175 @default.
• W2951567704 date "2016-09-23" @default.
• W2951567704 modified "2023-09-28" @default.
• W2951567704 title "Constraint-Based Clustering Selection" @default.
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• W2951567704 hasPublicationYear "2016" @default.
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