Toward databases mining: pre-processing collected data

Yan, X; Zhang, C; Zhang, S

Toward databases mining: pre-processing collected data

Yan, X Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: Applied Artificial Intelligence, 2003, 17 (5-6), pp. 545 - 561
Issue Date:: 2003-05-01

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Field	Value	Language
dc.contributor.author	Yan, 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	2003-05-01	en_US
dc.identifier.citation	Applied Artificial Intelligence, 2003, 17 (5-6), pp. 545 - 561	en_US
dc.identifier.issn	0883-9514	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3960
dc.description.abstract	This paper presents a new means of selecting quality data for mining multiple data sources. Traditional data-mining strategies obtain necessary data from internal and external data sources and pool all the data into a huge homogeneous dataset for discovery. In contrast, our data-mining strategy identifies quality data from (internal and external) data sources for a mining task. A framework is advocated for generating quality data. Experimental results demonstrate that application of this new data collecting technique can not only identify quality data, but can also efficiently reduce the amount of data that must be considered during mining. © 2003 Taylor and Francis Group, LLC.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343109
dc.relation.ispartof	Applied Artificial Intelligence	en_US
dc.relation.isbasedon	10.1080/713827171	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Toward databases mining: pre-processing collected data	en_US
dc.type	Journal Article
utslib.citation.volume	5-6	en_US
utslib.citation.volume	17	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	London, UK
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	5-6	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

This paper presents a new means of selecting quality data for mining multiple data sources. Traditional data-mining strategies obtain necessary data from internal and external data sources and pool all the data into a huge homogeneous dataset for discovery. In contrast, our data-mining strategy identifies quality data from (internal and external) data sources for a mining task. A framework is advocated for generating quality data. Experimental results demonstrate that application of this new data collecting technique can not only identify quality data, but can also efficiently reduce the amount of data that must be considered during mining. © 2003 Taylor and Francis Group, LLC.

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http://hdl.handle.net/10453/3960