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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Sanchez, M.A., Castillo, O., Castro, J.R. (2013). An Analysis on the Intrinsic Implementation of the Principle of Justifiable Granularity in Clustering Algorithms. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_10
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DOI: https://doi.org/10.1007/978-3-642-33021-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33020-9
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