Logic-based granular prototyping

Bargiela, A., Pedrycz, W. and Hirota, K., 2002. Logic-based granular prototyping. In: Proceedings of the 26th Annual International Computer Software and Applications. Los Alamitos, CA, USA: IEEE Computer Society, pp. 1164-1169. ISBN 0769517277

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Abstract

A fuzzy logic based similarity measure is introduced as a criterion for the identification of structure in data. An important characteristic of the proposed approach is that cluster prototypes are formed and evaluated in the course of the optimization without any a-priori assumptions about the number of clusters. The intuitively straightforward compound optimization criterion of maximizing the overall similarity between data and the prototypes while minimizing the similarity between the prototypes is adopted. It is shown that the partitioning of the pattern space obtained in the course of the optimization is more intuitive than the one obtained for the standard FCM. The local properties of clusters (in terms of the ranking order of features in the multidimensional pattern space) are captured by the weight vector associated with each cluster prototype. The weight vector is then used for the construction of interpretable information granules

Item Type: Chapter in book
Creators: Bargiela, A., Pedrycz, W. and Hirota, K.
Publisher: IEEE Computer Society
Place of Publication: Los Alamitos, CA, USA
Date: 2002
Divisions: Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 10:52
Last Modified: 19 Oct 2015 14:38
URI: http://irep.ntu.ac.uk/id/eprint/19479

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