Multiobjective evolutionary algorithm-based soft subspace clustering

Zhu, L; Cao, L; Yang, J

Multiobjective evolutionary algorithm-based soft subspace clustering

Zhu, L Cao, L

Yang, J

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Publication Type:: Conference Proceeding
Citation:: 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012
Issue Date:: 2012-10-04

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Field	Value	Language
dc.contributor.author	Zhu, L	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Yang, J	en_US
dc.date.issued	2012-10-04	en_US
dc.identifier.citation	2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012	en_US
dc.identifier.isbn	9781467315098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22989
dc.description.abstract	In this paper, a multiobjective evolutionary algorithm based soft subspace clustering, MOSSC, is proposed to simultaneously optimize the weighting within-cluster compactness and weighting between-cluster separation incorporated within two different clustering validity criteria. The main advantage of MOSSC lies in the fact that it effectively integrates the merits of soft subspace clustering and the good properties of the multiobjective optimization-based approach for fuzzy clustering. This makes it possible to avoid trapping in local minima and thus obtain more stable clustering results. Substantial experimental results on both synthetic and real data sets demonstrate that MOSSC is generally effective in subspace clustering and can achieve superior performance over existing state-of-the-art soft subspace clustering algorithms. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 IEEE Congress on Evolutionary Computation, CEC 2012	en_US
dc.relation.isbasedon	10.1109/CEC.2012.6252896	en_US
dc.title	Multiobjective evolutionary algorithm-based soft subspace clustering	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Brisbane, Australia	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, a multiobjective evolutionary algorithm based soft subspace clustering, MOSSC, is proposed to simultaneously optimize the weighting within-cluster compactness and weighting between-cluster separation incorporated within two different clustering validity criteria. The main advantage of MOSSC lies in the fact that it effectively integrates the merits of soft subspace clustering and the good properties of the multiobjective optimization-based approach for fuzzy clustering. This makes it possible to avoid trapping in local minima and thus obtain more stable clustering results. Substantial experimental results on both synthetic and real data sets demonstrate that MOSSC is generally effective in subspace clustering and can achieve superior performance over existing state-of-the-art soft subspace clustering algorithms. © 2012 IEEE.

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http://hdl.handle.net/10453/22989