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Cluster Analysis by Variance Ratio Criterion and Quantum-Behaved PSO

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Cloud Computing and Security (ICCCS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9483))

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Abstract

(Aim) A novel and efficient method based on the quantum-behaved particle swarm was proposed to solve the cluster analysis problem. (Methods) The QPSO was utilized to detect the optimal point of the VAriance RAtio Criterion (VARAC), which was created by us as fitness function in the optimization model. The experimental dataset had 4 groups (400 data in total) with three various degrees of overlapping: non-overlapping, partial overlapping, and intensely overlapping. The proposed QPSO was compared with traditional global optimization algorithms: genetic algorithm (GA), combinatorial particle swarm optimization (CPSO), and firefly algorithm (FA) via running 20 times. (Results) The results demonstrated that QPSO could locate the best VARAC values with the least time among the four algorithms. (Conclusions) We can find that QPSO performs effectively and fast for the problem of cluster analysis.

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Acknowledgment

This paper was supported by NSFC (610011024, 61273243, 51407095), Program of Natural Science Research of Jiangsu Higher Education Institutions (13KJB460011, 14KJB520021), Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), Key Supporting Science and Technology Program (Industry) of Jiangsu Province (BE2012201, BE2014009-3, BE2013012-2), Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province (BA2013058), Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), and Science Research Foundation of Hunan Provincial Education Department (12B023).

Conflict of interest

We have no conflicts of interest to disclose with regard to the subject matter of this paper.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, Jiangsu, China

    Shuihua Wang, Xingxing Zhou, Guangshuai Zhang, Genlin Ji, Jiquan Yang & Yudong Zhang

  2. Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, 210042, Jiangsu, China

    Shuihua Wang, Genlin Ji, Jiquan Yang & Yudong Zhang

  3. Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA

    Zheng Zhang

  4. Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China

    Zeyuan Lu

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  1. Shuihua Wang
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Correspondence to Shuihua Wang or Yudong Zhang .

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Editors and Affiliations

  1. Nanjing University of Aeronautics and As, Nanjing, China

    Zhiqiu Huang

  2. Nanjing University of Information Scienc, Nanjing, China

    Xingming Sun

  3. Nanjing, China

    Junzhou Luo

  4. Nanjing University of Aeronautics and As, Nanjing, China

    Jian Wang

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Wang, S. et al. (2015). Cluster Analysis by Variance Ratio Criterion and Quantum-Behaved PSO. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds) Cloud Computing and Security. ICCCS 2015. Lecture Notes in Computer Science(), vol 9483. Springer, Cham. https://doi.org/10.1007/978-3-319-27051-7_24

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  • DOI: https://doi.org/10.1007/978-3-319-27051-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27050-0

  • Online ISBN: 978-3-319-27051-7

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