Abstract
In this paper, we consider the semi-supervised clustering problem, where the prior knowledge is formalized as the Cannot-Link (CL) and Must-Link (ML) pairwise constraints. We propose an algorithm called SemiSync that tackles this problem from a novel perspective: synchronization. The basic idea is to regard the data points as a set of (constrained) phase oscillators, and simulate their dynamics to form clusters automatically. SemiSync allows dynamically propagating the constraints to unlabelled data points driven by their local data distributions, which effectively boosts the clustering performance even if little prior knowledge is available. We experimentally demonstrate the effectiveness of the proposed method.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61403062, 61433014, 41601025), Science-Technology Foundation for Young Scientist of SiChuan Province (2016JQ0007), Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China (161062) and National key research and development program (2016YFB0502300).
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Zhang, Z., Kang, D., Gao, C., Shao, J. (2019). SemiSync: Semi-supervised Clustering by Synchronization. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_45
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DOI: https://doi.org/10.1007/978-3-030-18590-9_45
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