Translator Disclaimer
1 February 1992 Application of an adaptive fuzzy system to clustering and pattern recognition
Author Affiliations +
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-1) 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 is 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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Scott C. Newton and Sunanda Mitra "Application of an adaptive fuzzy system to clustering and pattern recognition", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992);


Temporal object identification via fuzzy models
Proceedings of SPIE (January 31 1992)
Pattern recognition using stochastic neural networks
Proceedings of SPIE (August 19 1993)
Pattern recognition using Hilbert space
Proceedings of SPIE (October 31 1992)
Prototype neural network pattern recognition testbed
Proceedings of SPIE (January 31 1991)
Propagation Of Uncertainty Using Neural Networks
Proceedings of SPIE (March 26 1989)

Back to Top