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Author (up) Berget, I.; Mevik, B.H.; Naes, T.    openurl
  Title New modifications and applications of fuzzy C-means methodology Type Journal Article
  Year 2008 Publication Computational Statistics & Data Analysis Abbrev Journal Comput. Stat. Data Anal.  
  Volume 52 Issue 5 Pages 2403-2418  
  Corporate Author Thesis  
  Address Norwegian Food Res Inst, MATFORSK, N-1430 As, Norway, Email: tormod.naes@matforsk.no  
  Keywords fuzzy clustering; FCM; noise clustering; non-linear regression; prediction sorting; sequential clustering; mixture models  
  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. (c) 2007 Elsevier B.V. All rights reserved.  
  Publisher Elsevier Science Bv Place Editor  
  Language English Summary Language Orig Title  
  Series Editor Series Title Abbrev Series Title  
  Series Volume Series Issue Edition  
  Issn 0167-9473 Isbn Medium  
  Area Expedition Conference  
  Notes ISI:000253178600009 Approved no  
  Location Torfinn Nome (torfinn.nome@umb.no)  
  Call Number Cigene @ torfinn.nome @ Serial 1298  
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