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A New Inverse Nth Gravitation Based Clustering Method for Data Classification

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Smart Health (ICSH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10219))

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Abstract

Data classification is one of the core technologies in the field of pattern recognition and machine learning, which is of great theoretical significance and application value. With the increasing improvement of data acquisition, storage, transmission means and the amount of data, how to extract the essential attribute data from massive data, data accurate classification has become an important research topic. Inverse nth n order gravitational field is essentially a generalization of the n order in the physics, which can effectively describe the interaction between all the particles in the gravitational field. This paper proposes a new inverse nth power gravitation (I-n-PG) based clustering method is proposed for data classification. Some randomly generated data samples as well as some well-known classification data sets are used for the verification of the proposed I-n-PG classifier. The experiments show that our proposed I-n-PG classifier performs very well on both of these two test sets.

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Acknowledgment

This work was supported by National Nature Science Foundation of China under the research project 61273290.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, 361024, China

    Huarong Xu, Li Hao, Chengjie Jianag & Ejaz Ul Haq

Authors
  1. Huarong Xu
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  2. Li Hao
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  3. Chengjie Jianag
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  4. Ejaz Ul Haq
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Corresponding author

Correspondence to Huarong Xu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Chunxiao Xing

  2. Tsinghua University, Beijing, China

    Yong Zhang

  3. Beijing Foreign Studies University, Beijing, China

    Ye Liang

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Xu, H., Hao, L., Jianag, C., Haq, E.U. (2017). A New Inverse Nth Gravitation Based Clustering Method for Data Classification. In: Xing, C., Zhang, Y., Liang, Y. (eds) Smart Health. ICSH 2016. Lecture Notes in Computer Science(), vol 10219. Springer, Cham. https://doi.org/10.1007/978-3-319-59858-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-59858-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59857-4

  • Online ISBN: 978-3-319-59858-1

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