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Clustering of noisy image data using an adaptive neuro-fuzzy systemIdentification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.
Document ID
19930049176
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
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
January 1, 1992
Publication Information
Publication: In: Intelligent robots and computer vision XI: Biological, neural net, and 3-D methods; Proceedings of the Meeting, Boston, MA, Nov. 18-20, 1992 (A93-33171 12-63)
Publisher: Society of Photo-Optical Instrumentation Engineers
Subject Category
Cybernetics
Accession Number
93A33173
Funding Number(s)
CONTRACT_GRANT: NAG9-509
Distribution Limits
Public
Copyright
Other

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