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Evolving Fuzzy Prototypes for Efficient Data Clustering

Ben Burdsall, Christophe Giraud-Carrier, Evolving Fuzzy Prototypes for Efficient Data Clustering. Proceedings of the Second International ICSC Symposium on Fuzzy Logic and Applications (ISFL'97). ISBN 3-906454-03-7, pp. 217–223. February 1997. PDF, 61 Kbytes.

Abstract

This paper proposes a novel, evolutionary approach to data clustering and classification which overcomes many of the limitations of traditional systems. The approach rests on the optimisation of both the number and positions of fuzzy prototypes using a real-valued genetic algorithm (GA). Because the GA acts on all classes at once, the system benefits naturally from global information about possible class interactions. In addition, the concept of a receptive field for each prototype is used to replace the classical distance- based membership function by an infinite fuzzy support, multi-dimensional, Gaussian function centred over the prototype and with unique variance in each dimension, reflecting the tightness of the cluster. Hence, the notion of nearest-neighbour is replaced by that of nearest-attracting prototype (NAP). The proposed model is a completely self-optimising, fuzzy system called GA-NAP.

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