New modifications and applications of fuzzy C means methodology
Publikasjonsdetaljer
Tidsskrift : Computational Statistics & Data Analysis , vol. 52 , p. 2403–2418–16 , 2008
Utgiver : Elsevier
Internasjonale standardnummer
:
Trykt
:
0167-9473
Elektronisk
:
1872-7352
Publikasjonstype : Vitenskapelig artikkel
Sak : 5
Lenker
:
DOI
:
doi.org/10.1016/j.csda.2007.10...
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Kjetil Aune
Bibliotekleder
kjetil.aune@nofima.no
Sammendrag
The fuzzy C-means (FCM) algorithm and various modifications of it with focus on practical applications in both industry and science are discussed. The general methodology is presented, as well as some well known and also some less known modifications. It is demonstrated that the simple structure of the FCM algorithm allows for cluster analysis with non-typical and implicitly defined distance measures. Examples are residual distance for regression purposes, prediction sorting and penalised clustering criteria. Specialised applications of fuzzy clustering to be used for a sequential clustering strategy and for semi-supervised clustering are also discussed.
