Possibilistic fuzzy co-clustering of large document collections
Pattern Recognition
New modifications and applications of fuzzy C-means methodology
Computational Statistics & Data Analysis
Embedded zerotree wavelets coding based on adaptive fuzzy clustering for image compression
Image and Vision Computing
Unsupervised Pixel Classification in Satellite Imagery: A Two-stage Fuzzy Clustering Approach
Fundamenta Informaticae
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
Density-weighted fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Analysis of microarray data using multiobjective variable string length genetic fuzzy clustering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Computers and Operations Research
Evolutionary Rough Parallel Multi-Objective Optimization Algorithm
Fundamenta Informaticae
Expert Systems with Applications: An International Journal
Unsupervised Pixel Classification in Satellite Imagery: A Two-stage Fuzzy Clustering Approach
Fundamenta Informaticae
Rough clustering using generalized fuzzy clustering algorithm
Pattern Recognition
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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