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Fast density peaks clustering algorithm in polar coordinate system

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Abstract

Density peaks clustering (DPC) algorithm provides an efficient method to quickly find cluster centers with decision graphs. In recent years, due to its unique parameters, no iteration, and good robustness, it has been widely studied and applied. However, it also has some shortcomings, such as no adaptability, inadaptability to high-dimensional data and accuracy is easily affected. For reducing the higher time complexity of DPC, we introduce the polar coordinates to DPC (PC-DPC). Firstly, obtain the distance from every point to third-party point and the cosine value of the angle formed with the third-party vector, and reorder the points by distances and cosine values. Then, select other points in the adjacent sequence number of each point to calculate distances, and build a sparse distance matrix. Finally, the sparse distance matrix is used as the input of DPC to obtain clustering results. Theoretical analysis and experiments show that, compared with DPC and other algorithms, PC-DPC greatly reduces running time of DPC while maintaining clustering precision.

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Acknowledgements

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

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Authors and Affiliations

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

    Chao Li, Shifei Ding, Xiao Xu, Shuying Du & Tianhao Shi

  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. Chao Li
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  2. Shifei Ding
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  3. Xiao Xu
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  4. Shuying Du
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  5. Tianhao Shi
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Correspondence to Shifei Ding or Xiao Xu.

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Li, C., Ding, S., Xu, X. et al. Fast density peaks clustering algorithm in polar coordinate system. Appl Intell 52, 14478–14490 (2022). https://doi.org/10.1007/s10489-022-03360-3

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