TY - GEN
T1 - Self-organizing leader clustering in a neural network using a fuzzy learning rule
AU - Newton, Scott C.
AU - Mitra, Sunanda
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
T2 - Adaptive Signal Processing
Y2 - 22 July 1991 through 24 July 1991
ER -