A new convergence proof of fuzzy c-means

被引:72
作者
Gröll, L [1 ]
Jäkel, J [1 ]
机构
[1] Forschungszentrum Karlsruhe, Inst Appl Comp Sci, D-76021 Karlsruhe, Germany
关键词
convergence; fuzzy clustering; fuzzy c-means (FCM); majorization principle for steplength;
D O I
10.1109/TFUZZ.2005.856560
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this letter, we give a new, more direct derivation of tire convergence properties of the fuzzy c-means (FCM) algorithm, using the equivalence between the original and reduced FCM criterion. From the point of view of the reduced criterion, the FCM algorithm is simply a steepest descent algorithm with variable steplength. We prove that steplength adjustment follows from the majorization principle for steplength. By applying the majorization principle we give a straightforward proof of global convergence. Further convergence properties follow immediately using known results of optimization theory.
引用
收藏
页码:717 / 720
页数:4
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