Co-clustering For Weblogs In Semantic Space

Zong, Y; Xu, G; Dolog, P; Zhang, Y; Liu, R

Co-clustering For Weblogs In Semantic Space

Zong, Y Xu, G Dolog, P Zhang, Y Liu, R

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Publisher:: Springer-verlag Berlin
Publication Type:: Conference
Citation:: Web Information System Engineering-wise 2010, 2010, 6488 pp. 120 - 127
Issue Date:: 2010-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Zong, Y
dc.contributor.author	Xu, G
dc.contributor.author	Dolog, P
dc.contributor.author	Zhang, Y
dc.contributor.author	Liu, R
dc.contributor.editor	Chen, L
dc.contributor.editor	Triantafillou, P
dc.contributor.editor	Suel, T
dc.date	2010-12-12
dc.date.accessioned	2015-01-14T01:09:53Z
dc.date.issued	2010-01
dc.identifier.citation	Web Information System Engineering-wise 2010, 2010, 6488 pp. 120 - 127
dc.identifier.issn	0302-9743
dc.identifier.uri	http://hdl.handle.net/10453/31514
dc.description.abstract	Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in the context of web usage mining, which is able
dc.publisher	Springer-verlag Berlin
dc.relation.ispartof	Web Information System Engineering-wise 2010
dc.relation.ispartof	11th International Conference on Web Information Systems Engineering
dc.subject	NA, Artificial Intelligence & Image Processing
dc.subject	NA; Artificial Intelligence & Image Processing
dc.title	Co-clustering For Weblogs In Semantic Space
dc.type	Conference
utslib.citation.volume	6488
utslib.location	Berlin
utslib.for	0805 Distributed Computing
dc.location.activity	Hong Kong, PEOPLES R CHINA
pubs.embargo.period	Not known
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.declined	1970-01-01T00:00:00.0+1000

Abstract:

Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in the context of web usage mining, which is able

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http://hdl.handle.net/10453/31514