TY - GEN

T1 - Self-organizing leader clustering in a neural network using a fuzzy learning rule

AU - Newton, Scott C.

AU - Mitra, Sunanda

N1 - Copyright:
Copyright 2004 Elsevier B.V., All rights reserved.

PY - 1991

Y1 - 1991

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0026398044&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0026398044

SN - 0819406937

T3 - Proceedings of SPIE - The International Society for Optical Engineering

SP - 331

EP - 337

BT - Proceedings of SPIE - The International Society for Optical Engineering

A2 - Haykin, Simon

PB - Publ by Int Soc for Optical Engineering

Y2 - 22 July 1991 through 24 July 1991

ER -