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Density Normalization in Density Peak Based Clustering

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Graph-Based Representations in Pattern Recognition (GbRPR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10310))

Abstract

As a promising clustering approach, the density peak (DP) based algorithm utilizes the data density and carefully designed distance to identify cluster centers and cluster members. The key to this approach is the density calculation, which has a significant impact on the clustering results. However, the original DP algorithm applies the local density to identify cluster centers directly, and fails to take into account the density difference among clusters. As a result, large-density clusters may be partitioned into multiple parts and small-density clusters are likely to be merged with other clusters. In this paper we introduce a density normalization step to deal with this problem, and show that the normalized density can be used to characterize cluster centers more accurately than the original one. In experiments on various datasets, our method is shown to improve the performance of different density kernels evidently.

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Acknowledgement

This work is supported in part by National Natural Science Foundation of China under Grant No. 61473045 and No. 41371425, and in part by China Scholarship Council.

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Authors and Affiliations

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

    Jian Hou

  2. ECLT, Università Ca’ Foscari Venezia, 30124, Venezia, Italy

    Jian Hou

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

    Hongxia Cui

Authors
  1. Jian Hou
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  2. Hongxia Cui
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Corresponding author

Correspondence to Jian Hou .

Editor information

Editors and Affiliations

  1. Università degli Studi di Salerno, Fisciano, Italy

    Pasquale Foggia

  2. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Università degli Studi di Salerno, Fisciano, Italy

    Mario Vento

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Hou, J., Cui, H. (2017). Density Normalization in Density Peak Based Clustering. In: Foggia, P., Liu, CL., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2017. Lecture Notes in Computer Science(), vol 10310. Springer, Cham. https://doi.org/10.1007/978-3-319-58961-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-58961-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58960-2

  • Online ISBN: 978-3-319-58961-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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