U3 - mining unordered embedded subtrees using TMG candidate generation

Hadzic, Fedja; Tan, Henry; Dillon, Tharam S.

doi:10.1109/WIIAT.2008.403

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Access Status

Open access

Authors

Hadzic, Fedja

Tan, Henry

Dillon, Tharam S.

Date

2008

Type

Conference Paper

Metadata

Show full item record

Citation

Hadzic, Fedja and Tan, Henry and Dillon, Tharam. 2008. U3 - mining unordered embedded subtrees using TMG candidate generation, in Zhang, C. and Cercone, N. and Jain, L. (ed), International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) 2008, Dec 9 2008, pp. 285-292. Sydney, Australia: Institute of Electrical and Electronics Engineers (IEEE) Computer Society.

Source Title

Proceedings of the international conference on web intelligence and intelligent agent technology (WI-IAT) 2008

Source Conference

International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) 2008

DOI

10.1109/WIIAT.2008.403

ISBN

9780769534961

Faculty

Curtin Business School

School of Information Systems

School

Centre for Extended Enterprises and Business Intelligence

Remarks

Copyright © 2008 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

URI

http://hdl.handle.net/20.500.11937/44704

Collection

Curtin Research Publications

Abstract

In this paper we present an algorithm for mining of unordered embedded subtrees. This is an importantproblem for association rule mining from semistructured documents, and it has important applications in many biomedical, web and scientific domains. The proposed U3 algorithm is an extension of our general tree model guided (TMG) candidate generation framework and it considers both transaction based and occurrence match support. Synthetic and real world data sets are used to experimentally demonstrate the efficiency of our approach to the problem, and the flexibility of our general TMG framework.