FCM: The fuzzy c-means clustering algorithm
Bezdek, Jim Ehrlich, R. Full, William E.
Bezdek, Jim
Ehrlich, R.
Full, William E.
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1984
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Keywords
Cluster analysis,Cluster validity,Fuzzy clustering,Fuzzy Qmodel,Least-squared errors,Computer programming languages - Fortran,Computer programs,Data processing - Natural sciences applications,Mathematical techniques - Fuzzy sets,Adjustable weighting factor,Fuzzy C-means (FCM) Clustering properties,Fuzzy partitions and prototypes,Generalized Least-square objective function,Geostatical data analysis,Computer programming
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Citation
James C. Bezdek, Robert Ehrlich, William Full, FCM: The fuzzy c-means clustering algorithm, Computers & Geosciences, Volume 10, Issues 2–3, 1984, Pages 191-203, ISSN 0098-3004, https://doi.org/10.1016/0098-3004(84)90020-7.
Abstract
This paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity. © 1984. © 2015 Elsevier B.V., All rights reserved.
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Pergamon
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Computers and Geosciences
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00983004