Mining dynamic databases by weighting

Zhang, S; Liu, L

Mining dynamic databases by weighting

Zhang, S Liu, L

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Publication Type:: Journal Article
Citation:: Acta Cybernetica, 2003, 16 (1), pp. 179 - 205
Issue Date:: 2003-01-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Liu, L	en_US
dc.date.issued	2003-01-01	en_US
dc.identifier.citation	Acta Cybernetica, 2003, 16 (1), pp. 179 - 205	en_US
dc.identifier.issn	0324-721X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12382
dc.description.abstract	A dynamic database is a set of transactions, in which the content and the size can change over time. There is an essential difference between dynamic database mining and traditional database mining. This is because recently added transactions can be more 'interesting' than those inserted long ago in a dynamic database. This paper presents a method for mining dynamic databases. This approach uses weighting techniques to increase efficiency, enabling us to reuse frequent itemsets mined previously. This model also considers the novelty of itemsets when assigning weights. In particular, this method can find a kind of new patterns from dynamic databases, referred to trend patterns. To evaluate the effectiveness and efficiency of the proposed method, we implemented our approach and compare it with existing methods.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343109
dc.relation.ispartof	Acta Cybernetica	en_US
dc.subject.classification	Computation Theory & Mathematics	en_US
dc.title	Mining dynamic databases by weighting	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	16	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Lubeck, Germany
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	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	16	en_US

Abstract:

A dynamic database is a set of transactions, in which the content and the size can change over time. There is an essential difference between dynamic database mining and traditional database mining. This is because recently added transactions can be more 'interesting' than those inserted long ago in a dynamic database. This paper presents a method for mining dynamic databases. This approach uses weighting techniques to increase efficiency, enabling us to reuse frequent itemsets mined previously. This model also considers the novelty of itemsets when assigning weights. In particular, this method can find a kind of new patterns from dynamic databases, referred to trend patterns. To evaluate the effectiveness and efficiency of the proposed method, we implemented our approach and compare it with existing methods.

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http://hdl.handle.net/10453/12382