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Our research addresses the problem of selecting good constraints for semi-supervised clustering algorithms. For this purpose, we propose an active learning algorithm for the constraints collection task, which relies on the min-max algorithm and peaks estimation based on density score. Experiments conducted on some real data sets from UCI show the effectiveness of our approach. |
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