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A robust density peaks clustering algorithm using fuzzy neighborhood

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Abstract

The density peaks (DP) clustering approach, a novel density-based clustering algorithm, detects clusters with arbitrary shape. However, this method uses a crisp neighborhood relation to calculate local density. It cannot identify the different values of the neighborhood membership degrees of the points with respect to different distances from core point. The proposed FN-DP (fuzzy neighborhood density peaks) clustering algorithm uses fuzzy neighborhood relation to define the local density in FJP (fuzzy joint points) algorithm. The proposed algorithm integrates the speed of DP clustering algorithm with the robustness of FJP algorithm. The experimental results illustrate the superior performance of our algorithm compared with the DP clustering approach.

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Acknowledgements

The work is supported by the National Natural Science Foundation of China under Grant No. 61379101 and No. 61672522, the National Basic Research Program of China under Grant 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, China

    Mingjing Du & Shifei Ding

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 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. Yu Xue
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Correspondence to Shifei Ding.

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Du, M., Ding, S. & Xue, Y. A robust density peaks clustering algorithm using fuzzy neighborhood. Int. J. Mach. Learn. & Cyber. 9, 1131–1140 (2018). https://doi.org/10.1007/s13042-017-0636-1

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  • DOI: https://doi.org/10.1007/s13042-017-0636-1

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