SUN Liping, LUO Yonglong, ZHENG Xiaoyao, et al., “Gravitational Inspired Spectral Clustering with Constraint,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 487-491, 2015, doi: 10.1049/cje.2015.07.008
Citation: SUN Liping, LUO Yonglong, ZHENG Xiaoyao, et al., “Gravitational Inspired Spectral Clustering with Constraint,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 487-491, 2015, doi: 10.1049/cje.2015.07.008

Gravitational Inspired Spectral Clustering with Constraint

doi: 10.1049/cje.2015.07.008
Funds:  This work is supported by National Natural Science Foundation of China (No.61370050), and Research Program of Anhui Province Education Department (No.KJ2014A088).
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  • Corresponding author: LUO Yonglong (corresponding author) was born in 1972. He received his Ph.D. degree on Computer Science and Technology in 2005. Currently, he is the professor and Ph.D. supervisor of Anhui Normal University. His research interests include information security and spatial data processing. (Email:
  • Received Date: 2014-11-14
  • Rev Recd Date: 2015-01-21
  • Publish Date: 2015-07-10
  • Spectral clustering with pairwise constraints (i.e. mustlink and cannotlink) has been a hot topic in the machine learning community in recent years. Its performances are significantly influenced by utilizing the constraints. To make full use of the constraints' effect, pairwise constraints are integrated into an affinity matrix based on the gravitational method. In the data set as input, each point has mass property, and interacts with each other according to the universal law of gravitation. A Gravitational inspired constrained spectral clustering (GCSC) algorithm is proposed in this paper. Our algorithm is evaluated on multiple benchmark classification datasets. Compared with the existing approaches, experimental results demonstrate the effectiveness of our presented algorithm.
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