EDUA: An efficient algorithm for dynamic database mining

Zhang, S; Zhang, J; Zhang, C

EDUA: An efficient algorithm for dynamic database mining

Zhang, S Zhang, J Zhang, C

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Publication Type:: Journal Article
Citation:: Information Sciences, 2007, 177 (13), pp. 2756 - 2767
Issue Date:: 2007-07-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2007-07-01	en_US
dc.identifier.citation	Information Sciences, 2007, 177 (13), pp. 2756 - 2767	en_US
dc.identifier.issn	0020-0255	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4123
dc.description.abstract	Maintaining frequent itemsets (patterns) is one of the most important issues faced by the data mining community. While many algorithms for pattern discovery have been developed, relatively little work has been reported on mining dynamic databases, a major area of application in this field. In this paper, a new algorithm, namely the Efficient Dynamic Database Updating Algorithm (EDUA), is designed for mining dynamic databases. It works well when data deletion is carried out in any subset of a database that is partitioned according to the arrival time of the data. A pruning technique is proposed for improving the efficiency of the EDUA algorithm. Extensive experiments are conducted to evaluate the proposed approach and it is demonstrated that the EDUA is efficient. © 2007 Elsevier Inc. All rights reserved.	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	Information Sciences	en_US
dc.relation.isbasedon	10.1016/j.ins.2007.01.034	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	EDUA: An efficient algorithm for dynamic database mining	en_US
dc.type	Journal Article
utslib.citation.volume	13	en_US
utslib.citation.volume	177	en_US
utslib.for	080604 Database Management	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	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	13	en_US
pubs.publication-status	Published	en_US
pubs.volume	177	en_US

Abstract:

Maintaining frequent itemsets (patterns) is one of the most important issues faced by the data mining community. While many algorithms for pattern discovery have been developed, relatively little work has been reported on mining dynamic databases, a major area of application in this field. In this paper, a new algorithm, namely the Efficient Dynamic Database Updating Algorithm (EDUA), is designed for mining dynamic databases. It works well when data deletion is carried out in any subset of a database that is partitioned according to the arrival time of the data. A pruning technique is proposed for improving the efficiency of the EDUA algorithm. Extensive experiments are conducted to evaluate the proposed approach and it is demonstrated that the EDUA is efficient. © 2007 Elsevier Inc. All rights reserved.

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http://hdl.handle.net/10453/4123