On the convergence of some possibilistic clustering algorithms
- Publication Type:
- Journal Article
- Fuzzy Optimization and Decision Making, 2013, 12 (4), pp. 415 - 432
- Issue Date:
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.
Please use this identifier to cite or link to this item: