Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415355 | Computational Statistics & Data Analysis | 2008 | 16 Pages |
Abstract
The fuzzy CC-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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Ingunn Berget, Bjørn-Helge Mevik, Tormod Næs,