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Density peaks clustering using geodesic distances

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Abstract

Density peaks clustering (DPC) algorithm is a novel clustering algorithm based on density. It needs neither iterative process nor more parameters. However, it cannot effectively group data with arbitrary shapes, or multi-manifold structures. To handle this drawback, we propose a new density peaks clustering, i.e., density peaks clustering using geodesic distances (DPC-GD), which introduces the idea of the geodesic distances into the original DPC method. By experiments on synthetic data sets, we reveal the power of the proposed algorithm. By experiments on image data sets, we compared our algorithm with classical methods (kernel k-means algorithm and spectral clustering algorithm) and the original algorithm in accuracy and NMI. Experimental results show that our algorithm is feasible and effective.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61379101 and 61672522), the National Key Basic Research Program of China (No. 2013CB329502). The Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD), and the Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET).

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Authors and Affiliations

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

    Mingjing Du, Shifei Ding & Xiao Xu

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100090, China

    Shifei Ding

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yu Xue

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  1. Mingjing Du
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  2. Shifei Ding
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  3. Xiao Xu
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  4. Yu Xue
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Correspondence to Shifei Ding.

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Du, M., Ding, S., Xu, X. et al. Density peaks clustering using geodesic distances. Int. J. Mach. Learn. & Cyber. 9, 1335–1349 (2018). https://doi.org/10.1007/s13042-017-0648-x

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