A database-independent approach of mining association rules with genetic algorithm

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dc.contributor.author Yan, X
dc.contributor.author Zhang, C
dc.contributor.author Zhang, S
dc.date.accessioned 2009-11-09T02:45:19Z
dc.date.issued 2004
dc.identifier.citation Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, 2690 pp. 882 - 886
dc.identifier.issn 0302-9743
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/1772
dc.description.abstract Apriori-like algorithms for association rules mining rely upon the minimum support and the minimum confidence. Users often feel hard to give these thresholds. On the other hand, genetic algorithm is effective for global searching, especially when the searching space is so large that it is hardly possible to use deterministic searching method. We try to apply genetic algorithm to the association rules mining and propose an evolutionary method. Computations are conducted, showing that our ARMGA model can be used for the automation of the association rule mining systems, and the ideas given in this paper are effective. © Springer-Verlag 2003.
dc.title A database-independent approach of mining association rules with genetic algorithm
dc.type Conference Proceeding
dc.parent Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.journal.volume 2690
dc.journal.number en_US
dc.publocation Germany en_US
dc.publocation Los Alamitos, California
dc.identifier.startpage 882 en_US
dc.identifier.endpage 886 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.conference International Conference on Information Visualisation
dc.conference.location Hong Kong, China en_US
dc.for 080101 Adaptive Agents and Intelligent Robotics
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 020030
dc.personcode 011221
dc.percentage 70 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.custom International Conference on Intelligent Data Engineering and Automated Learning en_US
dc.date.activity 20030321 en_US
dc.date.activity 2003-07-16
dc.location.activity Hong Kong, China en_US
dc.location.activity London, UK
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
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history Closed (ID: 3)
utslib.collection.history General Collection (ID: 346) [2015-05-15T14:12:04+10:00]


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