An XML-Enabled Association Rule Framework

Feng, L; Dillon, TS; Weigand, H; Chang, E

An XML-Enabled Association Rule Framework

Feng, L Dillon, TS Weigand, H Chang, E

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Publisher:: Springer-Verlag Berlin Heidelberg
Publication Type:: Conference Proceeding
Citation:: Database and Expert Systems Applications, 2003, pp. 88 - 97
Issue Date:: 2003-01

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Field	Value	Language
dc.contributor.author	Feng, L	en_US
dc.contributor.author	Dillon, TS	en_US
dc.contributor.author	Weigand, H	en_US
dc.contributor.author	Chang, E	en_US
dc.contributor.editor	Marik, V	en_US
dc.contributor.editor	Retschitzegger, W	en_US
dc.contributor.editor	Stepankova, O	en_US
dc.date	2003-09-01	en_US
dc.date.issued	2003-01	en_US
dc.identifier.citation	Database and Expert Systems Applications, 2003, pp. 88 - 97	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1826
dc.description.abstract	With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract knowledge from XML data sources becomes increasingly important and desirable. This paper aims to integrate the newly emerging XML technology with data mining technology, using association rule mining as a case in point. Compared with traditional association mining in the well-structured world (e.g., relational databases), mining from XML data is faced with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. To tackle these challenges, in this paper, we propose an extended XML-enabled association rule framework, which is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.	en_US
dc.publisher	Springer-Verlag Berlin Heidelberg	en_US
dc.relation.ispartof	Database and Expert Systems Applications	en_US
dc.relation.ispartof	International Conference on Database and Expert Systems Applications	en_US
dc.relation.isbasedon	10.1007/978-3-540-45227-0_10	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	An XML-Enabled Association Rule Framework	en_US
dc.type	Conference Proceeding
utslib.location	Germany	en_US
utslib.location.activity	Prague, Czech Republic	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Prague, Czech Republic	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Germany	en_US
pubs.start-date	2003-09-01	en_US

Abstract:

With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract knowledge from XML data sources becomes increasingly important and desirable. This paper aims to integrate the newly emerging XML technology with data mining technology, using association rule mining as a case in point. Compared with traditional association mining in the well-structured world (e.g., relational databases), mining from XML data is faced with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. To tackle these challenges, in this paper, we propose an extended XML-enabled association rule framework, which is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/1826