Mining dependent patterns in probabilistic databases

Zhang, S; Zhang, C; Yu, JX

Mining dependent patterns in probabilistic databases

Zhang, S Zhang, C

Yu, JX

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Publication Type:: Journal Article
Citation:: Cybernetics and Systems, 2004, 35 (4), pp. 399 - 424
Issue Date:: 2004-06-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Yu, JX https://orcid.org/0000-0002-9738-827X	en_US
dc.date.issued	2004-06-01	en_US
dc.identifier.citation	Cybernetics and Systems, 2004, 35 (4), pp. 399 - 424	en_US
dc.identifier.issn	0196-9722	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4020
dc.description.abstract	Today's database systems must deal with uncertainty in the data they store. Consequently, there is a strong need for mining probabilistic databases. Because probabilistic data in first normal form relations is redundant, existing mining techniques are inadequate for discovering probabilistic databases. This paper designs a new strategy for identifying potentially useful patterns in probabilistic databases. A dependent rule is thus identified in a probabilistic database, represented in the form X → Y with conditional probability matrix MY/X This method uses an instance selection to increase efficiency, enabling us to reduce the search space. We evaluated the proposed technique, and our experimental results demonstrate that the approach is effective and efficient.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343109
dc.relation.ispartof	Cybernetics and Systems	en_US
dc.relation.isbasedon	10.1080/01969720496443390	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Mining dependent patterns in probabilistic databases	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	35	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	35	en_US

Abstract:

Today's database systems must deal with uncertainty in the data they store. Consequently, there is a strong need for mining probabilistic databases. Because probabilistic data in first normal form relations is redundant, existing mining techniques are inadequate for discovering probabilistic databases. This paper designs a new strategy for identifying potentially useful patterns in probabilistic databases. A dependent rule is thus identified in a probabilistic database, represented in the form X → Y with conditional probability matrix MY/X This method uses an instance selection to increase efficiency, enabling us to reduce the search space. We evaluated the proposed technique, and our experimental results demonstrate that the approach is effective and efficient.

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http://hdl.handle.net/10453/4020