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A Kernel-Based Membrane Clustering Algorithm

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Enjoying Natural Computing

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11270))

Abstract

The existing membrane clustering algorithms may fail to handle the data sets with non-spherical cluster boundaries. To overcome the shortcoming, this paper introduces kernel methods into membrane clustering algorithms and proposes a kernel-based membrane clustering algorithm, KMCA. By using non-linear kernel function, samples in original data space are mapped to data points in a high-dimension feature space, and the data points are clustered by membrane clustering algorithms. Therefore, a data clustering problem is formalized as a kernel clustering problem. In KMCA algorithm, a tissue-like P system is designed to determine the optimal cluster centers for the kernel clustering problem. Due to the use of non-linear kernel function, the proposed KMCA algorithm can well deal with the data sets with non-spherical cluster boundaries. The proposed KMCA algorithm is evaluated on nine benchmark data sets and is compared with four existing clustering algorithms.

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Acknowledgment

This work was partially supported by the National Natural Science Foundation of China (No. 61472328), Chunhui Project Foundation of the Education Department of China (Nos. Z2016143 and Z2016148), the Innovation Fund of Postgraduate, Xihua University (No. ycjj2018184), and Research Foundation of the Education Department of Sichuan province (No. 17TD0034), China.

Author information

Authors and Affiliations

  1. School of Computer and Software Engineering, Xihua University, Chengdu, 610039, Sichuan, China

    Jinyu Yang, Ru Chen, Guozhou Zhang & Hong Peng

  2. School of Electrical and Information Engineering, Xihua University, Chengdu, 610039, Sichuan, China

    Jun Wang

  3. Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, 41012, Sevilla, Spain

    Agustín Riscos-Núñez

Authors
  1. Jinyu Yang
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  2. Ru Chen
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  3. Guozhou Zhang
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  4. Hong Peng
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  5. Jun Wang
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  6. Agustín Riscos-Núñez
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Corresponding author

Correspondence to Hong Peng .

Editor information

Editors and Affiliations

  1. University of Seville, Sevilla, Spain

    Carmen Graciani

  2. University of Seville, Sevilla, Spain

    Agustín Riscos-Núñez

  3. Romanian Academy, Bucharest, Romania

    Gheorghe Păun

  4. Leiden University, Leiden, The Netherlands

    Grzegorz Rozenberg

  5. Turku Centre for Computer Science, Turku, Finland

    Arto Salomaa

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Yang, J., Chen, R., Zhang, G., Peng, H., Wang, J., Riscos-Núñez, A. (2018). A Kernel-Based Membrane Clustering Algorithm. In: Graciani, C., Riscos-Núñez, A., Păun, G., Rozenberg, G., Salomaa, A. (eds) Enjoying Natural Computing. Lecture Notes in Computer Science(), vol 11270. Springer, Cham. https://doi.org/10.1007/978-3-030-00265-7_25

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  • DOI: https://doi.org/10.1007/978-3-030-00265-7_25

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