Abstract
This paper presents an unsupervised fuzzy neural network which can be used for clustering and classification of complex data sets. The Integrated Adaptive Fuzzy Clustering (IAFC) architecture uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) with a new learning rule and a new similarity measure. We compare IAFC with other fuzzy ART-type clustering algorithms. The critical parameters in the operation of the IAFC are discussed. The Anderson's iris data are used to show the performance of the algorithm in comparison with other clustering algorithms.
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