Computing the minimum-support for mining frequent patterns

Zhang, S; Wu, X; Zhang, C; Lu, J

Computing the minimum-support for mining frequent patterns

Zhang, S Wu, X Zhang, C

Lu, J

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Publication Type:: Journal Article
Citation:: Knowledge and Information Systems, 2008, 15 (2), pp. 233 - 257
Issue Date:: 2008-05-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Lu, J	en_US
dc.date.issued	2008-05-01	en_US
dc.identifier.citation	Knowledge and Information Systems, 2008, 15 (2), pp. 233 - 257	en_US
dc.identifier.issn	0219-1377	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9054
dc.description.abstract	Frequent pattern mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been recognized that setting the minimum-support is a difficult task to users. This can hinder the widespread applications of these algorithms. In this paper we propose a computational strategy for identifying frequent itemsets, consisting of polynomial approximation and fuzzy estimation. More specifically, our algorithms (polynomial approximation and fuzzy estimation) automatically generate actual minimum-supports (appropriate to a database to be mined) according to users' mining requirements. We experimentally examine the algorithms using different datasets, and demonstrate that our fuzzy estimation algorithm fittingly approximates actual minimum-supports from the commonly-used requirements. © Springer-Verlag London Limited 2007.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation	http://purl.org/au-research/grants/arc/DP0559536
dc.relation	http://purl.org/au-research/grants/arc/DP0667060
dc.relation.ispartof	Knowledge and Information Systems	en_US
dc.relation.isbasedon	10.1007/s10115-007-0081-7	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Computing the minimum-support for mining frequent patterns	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	15	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

Frequent pattern mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been recognized that setting the minimum-support is a difficult task to users. This can hinder the widespread applications of these algorithms. In this paper we propose a computational strategy for identifying frequent itemsets, consisting of polynomial approximation and fuzzy estimation. More specifically, our algorithms (polynomial approximation and fuzzy estimation) automatically generate actual minimum-supports (appropriate to a database to be mined) according to users' mining requirements. We experimentally examine the algorithms using different datasets, and demonstrate that our fuzzy estimation algorithm fittingly approximates actual minimum-supports from the commonly-used requirements. © Springer-Verlag London Limited 2007.

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http://hdl.handle.net/10453/9054