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

Scott C. Newton, Sunanda Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsSimon Haykin
PublisherPubl by Int Soc for Optical Engineering
Pages331-337
Number of pages7
ISBN (Print)0819406937
StatePublished - 1991
EventAdaptive Signal Processing - San Diego, CA, USA
Duration: Jul 22 1991Jul 24 1991

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1565
ISSN (Print)0277-786X

Conference

ConferenceAdaptive Signal Processing
CitySan Diego, CA, USA
Period07/22/9107/24/91

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