New modifications and applications of fuzzy C means methodology
Publication details
Journal : Computational Statistics & Data Analysis , vol. 52 , p. 2403–2418–16 , 2008
Publisher : Elsevier
International Standard Numbers
:
Printed
:
0167-9473
Electronic
:
1872-7352
Publication type : Academic article
Issue : 5
Links
:
DOI
:
doi.org/10.1016/j.csda.2007.10...
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Kjetil Aune
Chief Librarian
kjetil.aune@nofima.no
Summary
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.
