On the convergence of some possibilistic clustering algorithms

Zhou, J; Cao, L; Yang, N

On the convergence of some possibilistic clustering algorithms

Zhou, J Cao, L

Yang, N

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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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Field	Value	Language
dc.contributor.author	Zhou, J	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Yang, N	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Fuzzy Optimization and Decision Making, 2013, 12 (4), pp. 415 - 432	en_US
dc.identifier.issn	1568-4539	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26579
dc.description.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. © 2013 Springer Science+Business Media New York.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP130102691
dc.relation.ispartof	Fuzzy Optimization and Decision Making	en_US
dc.relation.isbasedon	10.1007/s10700-013-9159-8	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	On the convergence of some possibilistic clustering algorithms	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	12	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

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. © 2013 Springer Science+Business Media New York.

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http://hdl.handle.net/10453/26579