A similarity measure on tree structured business data

Publication Type:
Conference Proceeding
ACIS 2012 : Proceedings of the 23rd Australasian Conference on Information Systems, 2012
Issue Date:
Filename Description Size
Thumbnail2011008032OK.pdf Published version417.65 kB
Adobe PDF
Full metadata record
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. Dianshuang Wu, Guangquan Zhang, Jie Lu, Wolfgang A. Halang © 2012.
Please use this identifier to cite or link to this item: