Fuzzy similarity measure model for trees with duplicated attributes

Wu, D; Zhang, G

Fuzzy similarity measure model for trees with duplicated attributes

Wu, D

Zhang, G

Permalink

Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent and Soft Computing, 2011, 100 pp. 333 - 340
Issue Date:: 2011-09-23

Closed Access

	Filename	Description	Size
	2011000736OK.pdf	Published version	396.75 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	Wu, D https://orcid.org/0000-0002-2499-5159	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2011-09-23	en_US
dc.identifier.citation	Advances in Intelligent and Soft Computing, 2011, 100 pp. 333 - 340	en_US
dc.identifier.isbn	9783642228322	en_US
dc.identifier.issn	1867-5662	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31419
dc.description.abstract	In many business situations, complex user profiles are described by tree structures, and evaluating the similarity between these trees is essential in many applications, such as recommender systems. This paper proposes a fuzzy similarity measure model for trees with duplicated attributes. In this model, the conceptual similarity between attributes and the weights of nodes are expressed by linguistic terms. To deal with duplicated attributes in the trees, nodes with the same concept are clustered. The most conceptual corresponding cluster pairs among two trees are identified. Based on the corresponding cluster pairs, the conceptual similarity and the value similarity between two trees are evaluated, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. © 2011 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Advances in Intelligent and Soft Computing	en_US
dc.relation.isbasedon	10.1007/978-3-642-22833-9_40	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Fuzzy similarity measure model for trees with duplicated attributes	en_US
dc.type	Conference Proceeding
utslib.citation.volume	100	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Beijing, China
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	100	en_US

Abstract:

In many business situations, complex user profiles are described by tree structures, and evaluating the similarity between these trees is essential in many applications, such as recommender systems. This paper proposes a fuzzy similarity measure model for trees with duplicated attributes. In this model, the conceptual similarity between attributes and the weights of nodes are expressed by linguistic terms. To deal with duplicated attributes in the trees, nodes with the same concept are clustered. The most conceptual corresponding cluster pairs among two trees are identified. Based on the corresponding cluster pairs, the conceptual similarity and the value similarity between two trees are evaluated, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. © 2011 Springer-Verlag Berlin Heidelberg.

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

http://hdl.handle.net/10453/31419