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An improved density-based adaptive p-spectral clustering algorithm

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Abstract

As a generalization algorithm of spectral clustering, p-spectral clustering has gradually attracted extensive attention of researchers. Gaussian kernel function is generally used in traditional p-spectral clustering to construct the similarity matrix of data. However, the Gaussian kernel function based on Euclidean distance is not effective when the data-set is complex with multiple density peaks or the density distribution is uniform. In order to solve this problem, an improved Density-based adaptive p-spectral clustering algorithm (DAPSC) is proposed, the prior information is considering to adjust the similarity between sample points and strengthen the local correlation between data points. In addition, by combining the density canopy method to update the initial clustering center and the number of clusters, the algorithm sensitivity of the original p-spectral clustering caused by the two is weakened. By experiments on four artificial data-sets and 8F UCI data-sets, we show that the proposed DAPSC has strong adaptability and more accurate compared with the four baseline methods.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61672522 and No. 61976216.

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Authors and Affiliations

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

    Yanru Wang, Shifei Ding, Lijuan Wang & Ling Ding

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Shifei Ding

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  1. Yanru Wang
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  2. Shifei Ding
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  3. Lijuan Wang
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  4. Ling Ding
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Correspondence to Shifei Ding.

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Wang, Y., Ding, S., Wang, L. et al. An improved density-based adaptive p-spectral clustering algorithm. Int. J. Mach. Learn. & Cyber. 12, 1571–1582 (2021). https://doi.org/10.1007/s13042-020-01236-x

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