Interpretable information granules with Minkowski FCM

Mencar, C, Bargiela, A, Castellano, G and Fanelli, AM, 2004. Interpretable information granules with Minkowski FCM. In: Proceedings of the 23rd Annual Meeting of the North American Fuzzy Information Processing Society. Piscataway, NJ, USA: IEEE, pp. 456-461. ISBN 0780383761

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In this study, we investigate the interpretability of information granules that arise through the application of a Fuzzy C-Means algorithm equipped with general Minkowski metric. The paper offers a link between the classical use of Euclidean norm and the more recently reported Tchebychev norm in the context of FCM-based data granulation. In particular, we focus our attention on the topology of information granules that are derived for various alpha-cuts of the resulting fuzzy sets. We quantify deformation of the granules caused by interaction between the FCM prototypes by relating their actual shape to the ideal hyper-boxes. The analysis leads to a two level characterization of information granules: the core part that has a hyper-box shape and the residual part that has complex topology and does not convey any pattern regularity

Item Type: Chapter in book
Creators: Mencar, C., Bargiela, A., Castellano, G. and Fanelli, A.M.
Publisher: IEEE
Place of Publication: Piscataway, NJ, USA
Date: 2004
Volume: 1
Divisions: Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 09:56
Last Modified: 19 Oct 2015 14:25

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