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Graph based clustering with constraints and active learning

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In this paper, we propose a semisupervised graph based clustering algorithm that tries to use seeds and constraints in the clustering process, called MCSSGC. Moreover, we also introduce a simple but efficient active learning method to collect the constraints that can boost the performance of MCSSGC, named KMMFFQS. These obtained results show that the proposed algorithm can significantly improve the clustering process compared to some recent algorithms. |

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