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A possibilistic approach to clusteringFuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.
Document ID
19930066722
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Krishnapuram, Raghu
(NASA Headquarters Washington, DC United States)
Keller, James M.
(Missouri-Columbia Univ. Columbia, United States)
Date Acquired
August 16, 2013
Publication Date
May 1, 1993
Publication Information
Publication: IEEE Transactions on Fuzzy Systems
Volume: 1
Issue: 2
ISSN: 1063-6706
Subject Category
Cybernetics
Accession Number
93A50719
Distribution Limits
Public
Copyright
Other

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