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Interval Type-2 Fuzzy Possibilistic C-Means Optimization Using Particle Swarm Optimization

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Nature-Inspired Design of Hybrid Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 667))

Abstract

In this paper, we present optimization of the Interval Type-2 Fuzzy Possibilistic C-Means (IT2FPCM) algorithm using Particle Swarm Optimization (PSO), with the goal of automatically finding the optimal number of clusters and the optimal lower and upper limit of Fuzzy and Possibility exponents of weight of the of the IT2FPCM algorithm, and also the centroids of clusters of each dataset tested with the IT2FPCM algorithm optimized using PSO.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Elid Rubio & Oscar Castillo

Authors
  1. Elid Rubio
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  2. Oscar Castillo
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Correspondence to Elid Rubio .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Oscar Castillo

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Rubio, E., Castillo, O. (2017). Interval Type-2 Fuzzy Possibilistic C-Means Optimization Using Particle Swarm Optimization. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-47054-2_4

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  • Online ISBN: 978-3-319-47054-2

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