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An Analysis of the Relationship between the Size of the Clusters and the Principle of Justifiable Granularity in Clustering Algorithms

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Soft Computing Applications in Optimization, Control, and Recognition

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 294))

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Abstract

The initial process for the granulation of information is the clustering of data, once the relationships between this data have been found these become clusters, each cluster represents a coarse granule, whereas each data point represents a fine granule. All clustering algorithms find these relationships by different means, yet the notion of the principle of justifiable granularity is not considered by any of them, since it is a recent idea in the area of Granular Computing. This paper describes a first approach in the analysis of the relationship between the size of the clusters found and their intrinsic implementation of the principle of justifiable granularity. An analysis is done with two datasets, simplefit and iris, and two clustering algorithms, subtractive and granular gravitational.

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Authors and Affiliations

  1. Universidad Autonoma de Baja California, Tijuana, Mexico

    Mauricio A. Sanchez & Juan R. Castro

  2. Instituto Tecnologico de Tijuana, Tijuana, Mexico

    Oscar Castillo

Authors
  1. Mauricio A. Sanchez
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  2. Oscar Castillo
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  3. Juan R. Castro
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Corresponding author

Correspondence to Mauricio A. Sanchez .

Editor information

Editors and Affiliations

  1. , Graduate Program of Computer Science, Tijuana Institute of Technology, Calzada Tecnologico s/n, Tijuana, 22379, Mexico

    Patricia Melin

  2. , Graduate Program of Computer Science, Tijuana Institute of Technology, Calzada Tecnologico s/n, Tijuana, 22379, Mexico

    Oscar Castillo

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Sanchez, M.A., Castillo, O., Castro, J.R. (2013). An Analysis of the Relationship between the Size of the Clusters and the Principle of Justifiable Granularity in Clustering Algorithms. In: Melin, P., Castillo, O. (eds) Soft Computing Applications in Optimization, Control, and Recognition. Studies in Fuzziness and Soft Computing, vol 294. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35323-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-35323-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35322-2

  • Online ISBN: 978-3-642-35323-9

  • eBook Packages: EngineeringEngineering (R0)

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