A modular, unsupervised neural network architecture that can be used for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns online in a stable and efficient manner. The system used a control structure similar to that found in the adaptive resonance theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two-stage process: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid position from fuzzy C-means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The AFLC algorithm is applied to the Anderson iris data and laser-luminescent finger image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets.

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This paper presents a modular, unsupervised neural network architecture which can be used for clustering and classification of complex data sets. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a conventional fuzzy K-means clustering algorithm as a learning rule embedded within a control structure similar to that found in the Adaptive Resonance Theory (ART-i) network. AFLC adaptively clusters analog inputs into classes without a priori knowledge of the entire data set or of the number of clusters present in the data. The classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. It will be shown that the definition of the distance metric can be adjusted as necessary to fit the characteristics of the input data. The AFLC algorithm using two different distance definitions is discussed and then the operating characteristics are described. The performance of the algorithm is presented through application of the algorithm to clustering computer generated normally distributed data, the Anderson & Fisher Iris data, and data generated from projections of 3-D objects in constrained motion.

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