Agglomerative Learning Algorithms for General Fuzzy Min-Max Neural Network

  • Bogdan Gabrys


In this paper two agglomerative learning algorithms based on new similarity measures defined for hyperbox fuzzy sets are proposed. They are presented in a context of clustering and classification problems tackled using a general fuzzy min-max (GFMM) neural network. The proposed agglomerative schemes have shown robust behaviour in presence of noise and outliers and insensitivity to the order of training patterns presentation. The emphasis is also put on the complimentary features to the previously presented incremental learning scheme more suitable for on-line adaptation and dealing with large training data sets. The performance and other properties of the agglomerative schemes are illustrated using a number of artificial and real-world data sets.

pattern classification hierarchical clustering agglomerative learning neuro-fuzzy system hyperbox fuzzy sets 


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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Bogdan Gabrys
    • 1
  1. 1.Applied Computational Intelligence Research Unit, Division of Computing and Information SystemsUniversity of PaisleyPaisleyScotland, UK

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