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Research of semi-supervised spectral clustering based on constraints expansion

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Abstract

Semi-supervised learning has become one of the hotspots in the field of machine learning in recent years. It is successfully applied in clustering and improves the clustering performance. This paper proposes a new clustering algorithm, called semi-supervised spectral clustering based on constraints expansion (SSCCE). This algorithm expands the known constraints set, changes the similarity relation of the sample points through the density–sensitive path distance, and then combines with semi-supervised spectral clustering to cluster. The experimental results prove that SSCCE algorithm has good clustering effect.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 41074003, 60975039), and the Opening Foundation of Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (No. IIP2010-1).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding, Bingjuan Qi, Hongjie Jia, Hong Zhu & Liwen Zhang

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Shifei Ding

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  1. Shifei Ding
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Correspondence to Shifei Ding.

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Ding, S., Qi, B., Jia, H. et al. Research of semi-supervised spectral clustering based on constraints expansion. Neural Comput & Applic 22 (Suppl 1), 405–410 (2013). https://doi.org/10.1007/s00521-012-0911-8

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  • DOI: https://doi.org/10.1007/s00521-012-0911-8

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