New modifications and applications of fuzzy C-means methodology

Authors:
Ingunn Berget;Bjørn-Helge Mevik;Tormod Nís
Affiliations:
CIGENE, Norwegian University of Life Sciences (UMB), 1430 ís, Norway and MATFORSK, Norwegian Food Research Institute, Oslovegen 1, 1430 ís, Norway;Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (UMB), Norway;MATFORSK, Norwegian Food Research Institute, Oslovegen 1, 1430 ís, Norway and University of Oslo, Norway
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

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.