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Fuzzy Clustering with Fitness Predator Optimizer for Multivariate Data Problems

  • Conference paper
Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

Fuzzy c-means (FCM) is the most common fuzzy clustering model and uses an objective function to measure the desirability of partitions. However, if the data sets contain several noise points, or if the data sets are very high dimensional, the iteration process of optimization the FCM model often falls into local optima solution. To avoid this problem, this paper proposes a new hybrid fuzzy clustering algorithm that incorporates the Fitness Predator Optimizer (FPO) into the FCM model. FPO is a new bionic-inspired algorithm to avoid premature convergence for the multimodal optimization problem. The excellent probability of finding the global optimum of FPO enhances the quality of fuzzy clustering. Five benchmark data sets from the UCI Machine Learning Repository are used to compare the performances of proposed FPO-FCM with FCM and a hybrid swarm algorithm based on Quantum-behaved PSO. Experimental results show that the proposed approach could demonstrate the desirable performance and avoid the minimum local value of objective function for multivariate data type clustering problems.

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Author information

Authors and Affiliations

  1. Graduate School of Computer and Information Sciences, Hosei University, Tokyo, Japan

    Shiqin Yang & Yuji Sato

Authors
  1. Shiqin Yang
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  2. Yuji Sato
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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Yang, S., Sato, Y. (2014). Fuzzy Clustering with Fitness Predator Optimizer for Multivariate Data Problems. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_14

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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