On the convergence of some possibilistic clustering algorithms

Publication Type:
Journal Article
Citation:
Fuzzy Optimization and Decision Making, 2013, 12 (4), pp. 415 - 432
Issue Date:
2013-12-01
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In this paper, an analysis of the convergence performance is conducted for a class of possibilistic clustering algorithms (PCAs) utilizing the Zangwill convergence theorem. It is shown that under certain conditions the iterative sequence generated by a PCA converges, at least along a subsequence, to either a local minimizer or a saddle point of the objective function of the algorithm. The convergence performance of more general PCAs is also discussed. © 2013 Springer Science+Business Media New York.
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