IncSPADE

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dc.contributor.authorOmer Adam
dc.contributor.authorZailani Abdullah
dc.contributor.authorAmir Ngah
dc.contributor.authorKasypi Mokhtar
dc.contributor.authorWan Muhamad Amir Wan Ahmad
dc.contributor.authorTutut Herawan
dc.contributor.authorNoraziah Ahmad
dc.contributor.authorMustafa Mat Deris
dc.contributor.authorAbdul Razak Hamdan
dc.contributor.authorJemal H. Abawajy
dc.date.accessioned2017-04-09T05:02:41Z
dc.date.available2017-04-09T05:02:41Z
dc.date.issued2016
dc.description.abstractIn this paper we propose Incremental Sequential PAttern Discovery using Equivalence classes (IncSPADE) algorithm to mine the dynamic database without the requirement of re-scanning the database again. In order to evaluate this algorithm, we conducted the experiments against three different artificial datasets. The result shows that IncSPADE outperformed the benchmarked algorithm called SPADE up to 20%.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5373
dc.language.isoenen_US
dc.publisherSpringer International Publishing Switzerlanden_US
dc.subjectSequential patternen_US
dc.subjectIncrementalen_US
dc.subjectUpdatableen_US
dc.subjectDatabaseen_US
dc.titleIncSPADEen_US
dc.title.alternativeAn Incremental Sequential Pattern Mining Algorithm Based on SPADE Propertyen_US
dc.typeArticleen_US
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