A decremental algorithm for maintaining frequent itemsets in dynamic databases

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dc.contributor.author Zhang, S
dc.contributor.author Wu, X
dc.contributor.author Zhang, J
dc.contributor.author Zhang, C
dc.date.accessioned 2009-12-21T02:31:49Z
dc.date.issued 2005
dc.identifier.citation Lecture Notes in Computer Science, 2005, 3589 pp. 305 - 314
dc.identifier.issn 0302-9743
dc.identifier.other C1 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.
dc.language eng
dc.title A decremental algorithm for maintaining frequent itemsets in dynamic databases
dc.type Journal Article
dc.parent Lecture Notes in Computer Science
dc.journal.volume 3589
dc.journal.number en_US
dc.publocation Berlin, Germany en_US
dc.identifier.startpage 305 en_US
dc.identifier.endpage 314 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.for 0804 Data Format
dc.personcode 100507
dc.personcode 020030
dc.personcode 011221
dc.percentage 100 en_US
dc.classification.name Data Format en_US
dc.classification.type FOR-08 en_US
dc.custom 0.251 en_US
dc.description.keywords N/A en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems


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