Identifying approximate itemsets of interest in large databases

Zhang, C; Zhang, S; Webb, GI

Identifying approximate itemsets of interest in large databases

Zhang, C

Zhang, S Webb, GI

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Publication Type:: Journal Article
Citation:: Applied Intelligence, 2003, 18 (1), pp. 91 - 104
Issue Date:: 2003-01-01

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Field	Value	Language
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Webb, GI	en_US
dc.date.issued	2003-01-01	en_US
dc.identifier.citation	Applied Intelligence, 2003, 18 (1), pp. 91 - 104	en_US
dc.identifier.issn	0924-669X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4130
dc.description.abstract	This paper presents a method for discovering approximate frequent itemsets of interest in large scale databases. This method uses the central limit theorem to increase efficiency, enabling us to reduce the sample size by about half compared to previous approximations. Further efficiency is gained by pruning from the search space uninteresting frequent itemsets. In addition to improving efficiency, this measure also reduces the number of itemsets that the user need consider. The model and algorithm have been implemented and evaluated using both synthetic and real-world databases. Our experimental results demonstrate the efficiency of the approach.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343109
dc.relation.ispartof	Applied Intelligence	en_US
dc.relation.isbasedon	10.1023/A:1020995206763	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Identifying approximate itemsets of interest in large databases	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	18	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Crete, Greece
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	18	en_US

Abstract:

This paper presents a method for discovering approximate frequent itemsets of interest in large scale databases. This method uses the central limit theorem to increase efficiency, enabling us to reduce the sample size by about half compared to previous approximations. Further efficiency is gained by pruning from the search space uninteresting frequent itemsets. In addition to improving efficiency, this measure also reduces the number of itemsets that the user need consider. The model and algorithm have been implemented and evaluated using both synthetic and real-world databases. Our experimental results demonstrate the efficiency of the approach.

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http://hdl.handle.net/10453/4130