Rule synthesizing from multiple related databases

He, D; Wu, X; Zhu, X

Rule synthesizing from multiple related databases

He, D Wu, X Zhu, X

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6119 LNAI (PART 2), pp. 201 - 213
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	He, D	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Zhu, X	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6119 LNAI (PART 2), pp. 201 - 213	en_US
dc.identifier.isbn	3642136710	en_US
dc.identifier.isbn	9783642136719	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31625
dc.description.abstract	In this paper, we study the problem of rule synthesizing from multiple related databases where items representing the databases may be different, and the databases may not be relevant, or similar to each other. We argue that, for such multi-related databases, simple rule synthesizing without a detailed understanding of the databases is not able to reveal meaningful patterns inside the data collections. Consequently, we propose a two-step clustering on the databases at both item and rule levels such that the databases in the final clusters contain both similar items and similar rules. A weighted rule synthesizing method is then applied on each such cluster to generate final rules. Experimental results demonstrate that the new rule synthesizing method is able to discover important rules which can not be synthesized by other methods. © 2010 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-13672-6_20	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Rule synthesizing from multiple related databases	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	6119 LNAI	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0804 Data Format	en_US
dc.location.activity	Hyderabad, India
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/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	6119 LNAI	en_US

Abstract:

In this paper, we study the problem of rule synthesizing from multiple related databases where items representing the databases may be different, and the databases may not be relevant, or similar to each other. We argue that, for such multi-related databases, simple rule synthesizing without a detailed understanding of the databases is not able to reveal meaningful patterns inside the data collections. Consequently, we propose a two-step clustering on the databases at both item and rule levels such that the databases in the final clusters contain both similar items and similar rules. A weighted rule synthesizing method is then applied on each such cluster to generate final rules. Experimental results demonstrate that the new rule synthesizing method is able to discover important rules which can not be synthesized by other methods. © 2010 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/31625