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A New Proposal for a Granular Fuzzy C-Means Algorithm

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Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 601))

Abstract

Fuzzy clustering algorithms are able to find the centroids and partition matrices, but are predominantly numerical, although each cluster prototype can be considered as a granule of information it continues to be a numeric value, in order to give a similar representation structure data. Granular theory and clustering algorithms can be combined to achieve this goal, resulting in granular prototypes and granular matrices of belonging and a more reflective data structure.

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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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Corresponding author

Correspondence to Oscar Castillo .

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Editors and Affiliations

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

    Patricia Melin

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

    Oscar Castillo

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

    Janusz Kacprzyk

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Rubio, E., Castillo, O. (2015). A New Proposal for a Granular Fuzzy C-Means Algorithm. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-17747-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17746-5

  • Online ISBN: 978-3-319-17747-2

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