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Adaptive fuzzy leader clustering of complex data sets in pattern recognitionA modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.
Document ID
19930047274
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Newton, Scott C.
(NASA Lyndon B. Johnson Space Center Houston, TX, United States)
Pemmaraju, Surya
(NASA Lyndon B. Johnson Space Center Houston, TX, United States)
Mitra, Sunanda
(Texas Tech Univ. Lubbock, United States)
Date Acquired
August 16, 2013
Publication Date
September 1, 1992
Publication Information
Publication: IEEE Transactions on Neural Networks
Volume: 3
Issue: 5
ISSN: 1045-9227
Subject Category
Cybernetics
Accession Number
93A31271
Funding Number(s)
CONTRACT_GRANT: NAG9-509
Distribution Limits
Public
Copyright
Other

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