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An Improved Density Peak Clustering Algorithm

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Density based clustering is an important clustering approach due to its ability to generate clusters of arbitrary shapes. Among density based clustering algorithms, the density peak (DP) based algorithm is shown to a potential one with some attractive properties. The DP algorithm calculates the local density of each data, and then the distance of each data to its nearest neighbor with higher density. Based on these two measurements, the cluster centers can be isolated from the non-center data. As a result, the cluster centers can be identified relatively easily and the non-center data can be grouped into clusters efficiently. In this paper we study the influence of density kernels on the clustering results and present a new kernel. We also present a new cluster center selection criterion based on distance normalization. Our new algorithm is shown to be effective in experiments on ten datasets.

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Acknowledgement

This work is supported in part by the National Natural Science Foundation of China under Grant No. 61473045, and by the Natural Science Foundation of Liaoning Province under Grant Nos. 20170540013 and 20170540005.

Author information

Authors and Affiliations

  1. College of Engineering, Bohai University, Jinzhou, 121013, China

    Jian Hou

  2. College of Information Science, Bohai University, Jinzhou, 121013, China

    Xu E

Authors
  1. Jian Hou
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  2. Xu E
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Corresponding author

Correspondence to Jian Hou .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Hou, J., E, X. (2017). An Improved Density Peak Clustering Algorithm. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_24

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

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