FCM: The fuzzy c-means clustering algorithm
1984; Elsevier BV; Volume: 10; Issue: 2-3 Linguagem: Inglês
10.1016/0098-3004(84)90020-7
ISSN1873-7803
AutoresJames C. Bezdek, Robert Ehrlich, William E. Full,
Tópico(s)Data Mining Algorithms and Applications
ResumoThis 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.
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