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

  • Mauricio A. Sanchez
  • Oscar Castillo
  • Juan R. Castro
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 294)

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.

Keywords

justifiable granularity clustering algorithm subtractive granular gravitational 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mauricio A. Sanchez
    • 1
  • Oscar Castillo
    • 2
  • Juan R. Castro
    • 1
  1. 1.Universidad Autonoma de Baja CaliforniaTijuanaMexico
  2. 2.Instituto Tecnologico de TijuanaTijuanaMexico

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