Evolving Fuzzy Prototypes for Efficient Data Clustering

  title={Evolving Fuzzy Prototypes for Efficient Data Clustering},
  author={B. Burdsall and Christophe G. Giraud-Carrier},
number of prototypes used to represent each class, the position of each prototype within its class and the membership function associated with each prototype. 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 of the classes at once… CONTINUE READING
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