U3 - Mning Unordered Embedded Subtrees Using TMG Candidate Generation

Author

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

Author_Institution

Digital Ecosyst. & Bus. Intell. Inst., Curtin Univ. of Technol., Perth, WA

Volume

1

fYear

2008

fDate

9-12 Dec. 2008

Firstpage

285

Lastpage

292

Abstract

In this paper we present an algorithm for mining of unordered embedded subtrees. This is an important problem for association rule mining from semi-structured 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.

Keywords

data mining; document handling; tree data structures; U3 algorithm; association rule mining; semistructured document; tree model guided candidate generation; unordered embedded subtrees; Association rules; Australia; Bioinformatics; Character generation; Data mining; Databases; Ecosystems; Intelligent agent; Tree data structures; XML; Canonical Form; TMG; Tree Mining; Unordered Embedded Subtrees;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on

Conference_Location

Sydney, NSW

Print_ISBN

978-0-7695-3496-1

Type

conf

DOI

10.1109/WIIAT.2008.403

Filename

4740462