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An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation

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Abstract

Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant No. 61972179), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2020A1515011476), Guangdong Basic and Applied Basic Research Foundation(2021B1515120048).

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Authors and Affiliations

  1. Network & Educational Technology Center, Jinan University, Guangzhou, 510632, China

    Shuhua Zhu

  2. College of Art, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China

    Ziyang Li

  3. Granduate School, Jinan University, Guangzhou, 510632, China

    Gang Han

  4. School of Economics, Jinan University, Guangzhou, 510632, China

    Wanyi Gao

  5. Department of Computer Science, Jinan University, Guangzhou, 510632, China

    Qifen Yang, Xiaotian Wu & Yuhui Deng

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  1. Qifen Yang
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  2. Ziyang Li
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  5. Shuhua Zhu
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Correspondence to Ziyang Li.

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The original online version of this article was revised: the headers in Table 3 were interchanged.

Appendix: Notation

Appendix: Notation

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Yang, Q., Li, Z., Han, G. et al. An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation. J Supercomput 78, 14597–14625 (2022). https://doi.org/10.1007/s11227-022-04456-w

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