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- W2004305622 abstract "In this paper we introduce new models for semi-supervised clustering problem, in particular we address this problem from the representation space point of view. Given a dataset enhanced with constraints (typically must-link and cannot-link constraints) and any clustering algorithm, the proposed approach aims at learning a projection space for the dataset that satisfies not only the constraints but also the required objective of the clustering algorithm on unenhanced data. We propose a boosting framework to weight the constraints and infers successive projection spaces in such a way that algorithm performance is improved. We experiment this approach on standard UCI datasets and show the effectiveness of our algorithm." @default.
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- W2004305622 date "2011-12-01" @default.
- W2004305622 modified "2023-09-26" @default.
- W2004305622 title "Integrating Pairwise Constraints into Clustering Algorithms: Optimization-Based Approaches" @default.
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- W2004305622 doi "https://doi.org/10.1109/icdmw.2011.103" @default.
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