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Proceedings Paper

Self-organizing leader clustering in a neural network using a fuzzy learning rule
Author(s): Scott C. Newton; Sunanda Mitra
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Paper Abstract

This paper describes a modular, unsupervised neural network architecture that can be used for data clustering and classification. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The system consists of a fuzzy K-means 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 euclidean metric comparison stage. Due to the modular design of AFLC, the euclidean metric can be replaced with various other metric for improved performance in a particular problem. The AFLC algorithm and operating characteristics are described, and the algorithm is compared to fuzzy K-means for both computer generated and real data.

Paper Details

Date Published: 1 December 1991
PDF: 7 pages
Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); doi: 10.1117/12.49788
Show Author Affiliations
Scott C. Newton, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)

Published in SPIE Proceedings Vol. 1565:
Adaptive Signal Processing
Simon Haykin, Editor(s)

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