Similarity measure models and algorithms for hierarchical cases

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dc.contributor.author Wu, D
dc.contributor.author Lu, J
dc.contributor.author Zhang, G
dc.date.accessioned 2012-10-12T03:32:49Z
dc.date.issued 2011-01
dc.identifier.citation Expert Systems with Applications, 2011, 38 (12), pp. 15049 - 15056
dc.identifier.issn 0957-4174
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/17947
dc.description.abstract Many business situations such as events, products and services, are often described in a hierarchical structure. When we use case-based reasoning (CBR) techniques to support business decision-making, we require a hierarchical-CBR technique which can effectively compare and measure similarity between two hierarchical cases. This study first defines hierarchical case trees (HC-trees) and discusses related features. It then develops a similarity evaluation model which takes into account all the information on nodes structures, concepts, weights, and values in order to comprehensively compare two hierarchical case trees. A similarity measure algorithm is proposed which includes a node concept correspondence degree computation algorithm and a maximum correspondence tree mapping construction algorithm, for HC-trees. We provide two illustrative examples to demonstrate the effectiveness of the proposed hierarchical case similarity evaluation model and algorithms, and possible applications in CBR systems
dc.publisher Pergamon
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1016/j.eswa.2011.05.040
dc.title Similarity measure models and algorithms for hierarchical cases
dc.type Journal Article
dc.parent Expert Systems with Applications
dc.journal.volume 12
dc.journal.volume 38
dc.journal.number 12 en_US
dc.publocation United Kingdom en_US
dc.identifier.startpage 15049 en_US
dc.identifier.endpage 15056 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 0806 Information Systems
dc.for 0801 Artificial Intelligence and Image Processing
dc.for 0102 Applied Mathematics
dc.personcode 001038
dc.personcode 020014
dc.personcode 108178
dc.percentage 34 en_US
dc.classification.name Applied Mathematics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Hierarchical similarity
dc.description.keywords Hierarchical cases
dc.description.keywords Tree similarity measuring
dc.description.keywords Case-based reasoning
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
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
pubs.consider-herdc true
utslib.collection.history General (ID: 2)


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