CCE: A coupled framework of clustering ensembles

She, Z; Wang, C; Cao, L

CCE: A coupled framework of clustering ensembles

She, Z Wang, C Cao, L

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2012, 3 pp. 2455 - 2456
Issue Date:: 2012-11-07

Closed Access

	Filename	Description	Size
	2012001100OK.pdf	Published version	677.68 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	She, Z	en_US
dc.contributor.author	Wang, C	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2012-11-07	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2012, 3 pp. 2455 - 2456	en_US
dc.identifier.isbn	9781577355687	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30868
dc.description.abstract	Clustering ensemble mainly relies on the pairwise similarity to capture the consensus function. However, it usually considers each base clustering independently, and treats the similarity measure roughly with either 0 or 1. To address these two issues, we propose a coupled framework of clustering ensembles CCE, and exemplify it with the coupled version CCSPA for CSPA. Experiments demonstrate the superiority of CCS/34 over baseline approaches in terms of the clustering accuracy. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP1096218
dc.relation	http://purl.org/au-research/grants/arc/LP100200774
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.title	CCE: A coupled framework of clustering ensembles	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Toronto, Ontario, Canada
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.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

Clustering ensemble mainly relies on the pairwise similarity to capture the consensus function. However, it usually considers each base clustering independently, and treats the similarity measure roughly with either 0 or 1. To address these two issues, we propose a coupled framework of clustering ensembles CCE, and exemplify it with the coupled version CCSPA for CSPA. Experiments demonstrate the superiority of CCS/34 over baseline approaches in terms of the clustering accuracy. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/30868