On the convergence of some possibilistic clustering algorithms

Authors:
Jian Zhou;Longbing Cao;Nan Yang
Affiliations:
School of Management, Shanghai University, Shanghai, China 200444;Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia;School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China 200433
Venue:
Fuzzy Optimization and Decision Making
Year:
2013

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Abstract

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.