Is minimum-support appropriate to identifying large itemsets?

Zhang, S; Liu, L; Lu, J; Ou, Y

Is minimum-support appropriate to identifying large itemsets?

Zhang, S Liu, L Lu, J Ou, Y

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2004, 3157 pp. 474 - 484
Issue Date:: 2004-12-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Liu, L	en_US
dc.contributor.author	Lu, J	en_US
dc.contributor.author	Ou, Y	en_US
dc.date.issued	2004-12-01	en_US
dc.identifier.citation	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2004, 3157 pp. 474 - 484	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1761
dc.description.abstract	Apriori-like algorithms have been based on the assumption that users can specify the minimum-support for their databases. In this paper, we propose a fuzzy strategy for identifying interesting itemsets without specifying the true minimum-support. This strategy allows users to specify their mining requirements in commonly sentences. And our algorithm generates potentially useful itemsets in fuzzy sets. © Springer-Verlag Berlin Heidelberg 2004.	en_US
dc.relation.ispartof	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Is minimum-support appropriate to identifying large itemsets?	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3157	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Auckland, New Zealand	en_US
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	3157	en_US

Abstract:

Apriori-like algorithms have been based on the assumption that users can specify the minimum-support for their databases. In this paper, we propose a fuzzy strategy for identifying interesting itemsets without specifying the true minimum-support. This strategy allows users to specify their mining requirements in commonly sentences. And our algorithm generates potentially useful itemsets in fuzzy sets. © Springer-Verlag Berlin Heidelberg 2004.

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http://hdl.handle.net/10453/1761