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dc.contributor.authorPears, R
dc.contributor.authorKutty, S
dc.date.accessioned2013-02-26T04:37:34Z
dc.date.available2013-02-26T04:37:34Z
dc.date.copyright2007
dc.date.issued2013-02-26
dc.identifier.citation2007 ACS/IEEE International Conference on Computer Systems and Applications. AICCSA , Amman, Jordan, published in: Proceedings of the 2007 ACS/IEEE International Conference on Computer Systems and Applicationsm AICCSA, pp.424 - 431
dc.identifier.urihttp://hdl.handle.net/10292/5184
dc.description.abstractDespite advances in algorithmic design, association rule mining remains problematic from a performance viewpoint when the size of the underlying transaction database is large. The well-known a priori approach, while reducing the computational effort involved still suffers from the problem of scalability due to its reliance on generating candidate itemsets. In this paper we present a novel approach that combines the power of preprocessing with the application of user-defined constraints to prune the itemset space prior to building a compact FP-tree. Experimentation shows that that our algorithm significantly outperforms the current state of the art algorithm, FP-bonsai.
dc.publisherIEEE
dc.rightsCopyright © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.titleFGC: an efficient constraint-based frequent set miner
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1109/AICCSA.2007.370916
dc.identifier.roid5871en_NZ
aut.conference.typePaper Published in Proceedings
aut.relation.endpage431
aut.relation.startpage424
pubs.elements-id6063


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