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A similarity measure on tree structured business data

Wu, Dianshuang, Zhang, Guangquan, Lu, Jie and Halang, Wolfgang A. 2012, A similarity measure on tree structured business data, in ACIS 2012 : Location, location, location : Proceedings of the 23rd Australasian Conference on Information Systems 2012, ACIS, [Geelong, Vic.], pp. 1-10.

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Title A similarity measure on tree structured business data
Author(s) Wu, Dianshuang
Zhang, Guangquan
Lu, Jie
Halang, Wolfgang A.
Conference name Australasian Conference on Information Systems (23rd : 2012 : Geelong, Victoria)
Conference location Geelong, Victoria
Conference dates 3-5 Dec. 2012
Title of proceedings ACIS 2012 : Location, location, location : Proceedings of the 23rd Australasian Conference on Information Systems 2012
Editor(s) Lamp, JohnORCID iD for Lamp, John orcid.org/0000-0003-1891-0400
Publication date 2012
Conference series Australasian Conference on Information Systems
Start page 1
End page 10
Total pages 10
Publisher ACIS
Place of publication [Geelong, Vic.]
Keyword(s) tree similarity measure
tree mapping
one-to-many mapping
tree structured business data
Summary In many business situations, products or user profile data are so complex that they need to be described by use of tree structures. Evaluating the similarity between tree-structured data is essential in many applications, such as recommender systems. To evaluate the similarity between two trees, concept corresponding nodes should be identified by constructing an edit distance mapping between them. Sometimes, the intension of one concept includes the intensions of several other concepts. In that situation, a one-to-many mapping should be constructed from the point of view of structures. This paper proposes a tree similarity measure model that can construct this kind of mapping. The similarity measure model takes into account all the information on nodes’ concepts, weights, and values. The conceptual similarity and the value similarity between two trees are evaluated based on the constructed mapping, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. The effectiveness of the proposed similarity measure model is shown by an illustrative example and is also demonstrated by applying it into a recommender system.
Notes Reproduced with the kind permission of the copyright owner.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
Related work DU:30049020
Copyright notice ©2012, The Authors/ACIS
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049098

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