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Initial Seeds Selection in Dynamic Clustering Method Based on Data Depth

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques (IScIDE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9243))

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Abstract

Resorting to the theory of atomic models and the tool of data depth, we propose a novel method for initial seeds selection in dynamic clustering method. We define the cohesion of a point in a given data set, which includes the information of the significance and locations of neighboring points together. Then, the dynamic clustering algorithm based on cohesion is proposed. Compared with the density-based dynamic clustering algorithm, the clustering results demonstrate that our proposed method is more effective and robust.

The work is supported by Natural Science Foundation of Zhejiang Province of China (LY14A010003) AMS Subject Classification (2000):   Primary 62H30; Secondary 62-07.

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Author information

Authors and Affiliations

  1. School of Computer and Computing Science, Zhejiang University City College, Hangzhou, China

    Caiya Zhang

  2. Department of Statistics Science, Cornell University, Ithaca, USA

    Ze Jin

Authors
  1. Caiya Zhang
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  2. Ze Jin
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Corresponding author

Correspondence to Caiya Zhang .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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© 2015 Springer International Publishing Switzerland

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Zhang, C., Jin, Z. (2015). Initial Seeds Selection in Dynamic Clustering Method Based on Data Depth. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_60

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  • DOI: https://doi.org/10.1007/978-3-319-23862-3_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23861-6

  • Online ISBN: 978-3-319-23862-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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