Integrating pairwise constraints into clustering algorithms: optimization-based approaches

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.
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Conference papers
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Contributor : Jacques-Henri Sublemontier <>
Submitted on : Monday, October 17, 2011 - 10:46:37 AM
Last modification on : Thursday, January 17, 2019 - 3:06:06 PM


  • HAL Id : hal-00632952, version 1



Jacques-Henri Sublemontier, Lionel Martin, Guillaume Cleuziou, Matthieu Exbrayat. Integrating pairwise constraints into clustering algorithms: optimization-based approaches. OEDM 2011, Optimization based approaches for Emerging Data Mining problems, Dec 2011, Vancouver, Canada. pp._. ⟨hal-00632952⟩



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