Database classification for multi-database mining

Wu, X; Zhang, C; Zhang, S

Database classification for multi-database mining

Wu, X Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: Information Systems, 2005, 30 (1), pp. 71 - 88
Issue Date:: 2005-03-01

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Field	Value	Language
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2005-03-01	en_US
dc.identifier.citation	Information Systems, 2005, 30 (1), pp. 71 - 88	en_US
dc.identifier.issn	0306-4379	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5353
dc.description.abstract	Many large organizations have multiple databases distributed in different branches, and therefore multi-database mining is an important task for data mining. To reduce the search cost in the data from all databases, we need to identify which databases are most likely relevant to a data mining application. This is referred to as database selection. For real-world applications, database selection has to be carried out multiple times to identify relevant databases that meet different applications. In particular, a mining task may be without reference to any specific application. In this paper, we present an efficient approach for classifying multiple databases based on their similarity between each other. Our approach is application-independent. © 2003 Elsevier Ltd. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343109
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation.ispartof	Information Systems	en_US
dc.relation.isbasedon	10.1016/j.is.2003.10.001	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Database classification for multi-database mining	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	30	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

Many large organizations have multiple databases distributed in different branches, and therefore multi-database mining is an important task for data mining. To reduce the search cost in the data from all databases, we need to identify which databases are most likely relevant to a data mining application. This is referred to as database selection. For real-world applications, database selection has to be carried out multiple times to identify relevant databases that meet different applications. In particular, a mining task may be without reference to any specific application. In this paper, we present an efficient approach for classifying multiple databases based on their similarity between each other. Our approach is application-independent. © 2003 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/5353