IEEE/ICACT20220071 Slide.02        [Big Slide]       Oral Presentation
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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