A decremental algorithm for maintaining frequent itemsets in dynamic databases

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

A decremental algorithm for maintaining frequent itemsets in dynamic databases

Zhang, S Wu, X Zhang, J Zhang, C

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Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science, 2005, 3589 pp. 305 - 314
Issue Date:: 2005-10-24

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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, J	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2005-10-24	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2005, 3589 pp. 305 - 314	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4143
dc.description.abstract	Data mining and machine learning must confront the problem of pattern maintenance because data updating is a fundamental operation in data management. Most existing data-mining algorithms assume that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new data. While there are many efficient mining techniques for data additions to databases, in this paper, we propose a decrementai algorithm for pattern discovery when data is being deleted from databases. We conduct extensive experiments for evaluating this approach, and illustrate that the proposed algorithm can well model and capture useful interactions within data when the data is decreasing. © Springer-Verlag Berlin Heidelberg 2005.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A decremental algorithm for maintaining frequent itemsets in dynamic databases	en_US
dc.type	Journal Article
utslib.citation.volume	3589	en_US
utslib.for	0804 Data Format	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.publication-status	Published	en_US
pubs.volume	3589	en_US

Abstract:

Data mining and machine learning must confront the problem of pattern maintenance because data updating is a fundamental operation in data management. Most existing data-mining algorithms assume that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new data. While there are many efficient mining techniques for data additions to databases, in this paper, we propose a decrementai algorithm for pattern discovery when data is being deleted from databases. We conduct extensive experiments for evaluating this approach, and illustrate that the proposed algorithm can well model and capture useful interactions within data when the data is decreasing. © Springer-Verlag Berlin Heidelberg 2005.

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http://hdl.handle.net/10453/4143