@inproceedings{e6b791c8a7fe419ea1661be764391bb3,

title = "Self-organizing leader clustering in a neural network using a fuzzy learning rule",

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.",

author = "Newton, {Scott C.} and Sunanda Mitra",

year = "1991",

language = "English",

isbn = "0819406937",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "Publ by Int Soc for Optical Engineering",

pages = "331--337",

editor = "Simon Haykin",

booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

note = "null ; Conference date: 22-07-1991 Through 24-07-1991",

}