Title :
A New Convergence Proof of Fuzzy c-Means
Author :
Gröll, Lutz ; Jäkel, Jens
Author_Institution :
Inst. for Appl. Comput. Sci., Forschungszentrum Karlsruhe
Abstract :
In this letter, we give a new, more direct derivation of the 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
Keywords :
convergence; optimisation; pattern clustering; convergence proof; fuzzy c-means algorithm; optimization theory; reduced criterion; steepest descent algorithm; steplength adjustment; Closed-form solution; Clustering algorithms; Computer science; Convergence; Equations; Fuzzy sets; Optimization methods; Convergence; fuzzy c-means (FCM); fuzzy clustering; majorization principle for steplength;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
DOI :
10.1109/TFUZZ.2005.856560