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A Kernel-Based Membrane Clustering Algorithm

  • Jinyu Yang
  • Ru Chen
  • Guozhou Zhang
  • Hong PengEmail author
  • Jun Wang
  • Agustín Riscos-Núñez
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jinyu Yang
    • 1
  • Ru Chen
    • 1
  • Guozhou Zhang
    • 1
  • Hong Peng
    • 1
    Email author
  • Jun Wang
    • 2
  • Agustín Riscos-Núñez
    • 3
  1. 1.School of Computer and Software EngineeringXihua UniversityChengduChina
  2. 2.School of Electrical and Information EngineeringXihua UniversityChengduChina
  3. 3.Research Group of Natural Computing, Department of Computer Science and Artificial IntelligenceUniversity of SevilleSevillaSpain

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