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dc.contributor.authorTan, H.
dc.contributor.authorDillon, Tharam S.
dc.contributor.authorHadzic, Fedja
dc.contributor.authorChang, Elizabeth
dc.identifier.citationTan, Henry and Dillon, Tharam and Hadzic, Fedja and Chang, Elizabeth. 2006. : Razor: Mining distance-constrained embedded subtrees, in Tsumota, Shusaku (ed), IEEE International Conference on Data Mining Workshops, Dec 18 2006, pp. 8-13. Hong Kong: IEEE.

Our work is focused on the task of mining frequent subtrees from a database of rooted ordered labelled subtrees. Previously we have developed an efficient algorithm, MB3 [12], for mining frequent embedded subtrees from a database of rooted labeled and ordered subtrees. The efficiency comes from the utilization of a novel Embedding List representation for Tree Model Guided (TMG) candidate generation. As an extension the IMB3 [13] algorithm introduces the Level of Embedding constraint. In this study we extend our past work by developing an algorithm, Razor, for mining embedded subtrees where the distance of nodes relative to the root of the subtree needs to be considered. This notion of distance constrained embedded tree mining will have important applications in web information systems, conceptual model analysis and more sophisticated ontology matching. Domains representing their knowledge in a tree structured form may require this additional distance information as it commonly indicates the amount of specific knowledge stored about a particular concept within the hierarchy. The structure based approaches for schema matching commonly take the distance among the concept nodes within a sub-structure into account when evaluating the concept similarity across different schemas. We present an encoding strategy to efficiently enumerate candidate subtrees taking the distance of nodes relative to the root of the subtree into account. The algorithm is applied to both synthetic and real-world datasets, and the experimental results demonstrate the correctness and effectiveness of the proposed technique.

dc.subjectembedded subtree
dc.subjectstructure matching
dc.subjectmining with constraints
dc.subjectfrequent subtree mining
dc.subjectassociation mining
dc.titleRazor: Mining distance-constrained embedded subtrees
dc.typeConference Paper
dcterms.source.titleProceedings of the Sixth IEEE International Conference on Data Mining - Workshops
dcterms.source.seriesProceedings of the Sixth IEEE International Conference on Data Mining - Workshops
dcterms.source.conferenceIEEE International Conference on Data Mining Workshops
dcterms.source.conference-start-dateDec 18 2006
dcterms.source.conferencelocationHong Kong

Copyright 2006 IEEE


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curtin.departmentCentre for Extended Enterprises and Business Intelligence
curtin.accessStatusOpen access
curtin.facultyCurtin Business School

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