Similarity measure models and algorithms for hierarchical cases

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dc.contributor.author Wu, Dianshuang en_US
dc.contributor.author Lu, Jie en_US
dc.contributor.author Zhang, Guangquan en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:32:49Z
dc.date.available 2012-10-12T03:32:49Z
dc.date.issued 2011 en_US
dc.identifier 2011001013 en_US
dc.identifier.citation Wu Dianshuang, Lu Jie, and Zhang Guangquan 2011, 'Similarity measure models and algorithms for hierarchical cases', Pergamon, vol. 38, no. 12, pp. 15049-15056. en_US
dc.identifier.issn 0957-4174 en_US
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 en_US
dc.language en_US
dc.publisher Pergamon en_US
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.eswa.2011.05.040 en_US
dc.rights NOTICE: this is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications , [38 (2011) ] doi:10.1016/j.eswa.2011.05.040 en_US
dc.title Similarity measure models and algorithms for hierarchical cases en_US
dc.parent Expert Systems with Applications en_US
dc.journal.volume 38 en_US
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 010200 en_US
dc.personcode 11197353 en_US
dc.personcode 001038 en_US
dc.personcode 020014 en_US
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; Hierarchical cases; Tree similarity measuring; Case-based reasoning en_US
dc.staffid en_US
dc.staffid 020014 en_US


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