Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/38046
DC FieldValueLanguage
dc.contributor.authorJea, K.F.en_US
dc.contributor.author賈坤芳zh_TW
dc.contributor.authorChang, M.Y.en_US
dc.date2008zh_TW
dc.date.accessioned2014-06-06T08:00:26Z-
dc.date.available2014-06-06T08:00:26Z-
dc.identifier.issn0169-023Xzh_TW
dc.identifier.urihttp://hdl.handle.net/11455/38046-
dc.description.abstractTo speed up the task of association rule mining, a novel concept based on support approximation has been previously proposed for generating frequent itemsets. However, the mining technique utilized by this concept may incur unstable accuracy due to approximation error. To overcome this drawback, in this paper we combine a new clustering method with support approximation, and propose a mining method, namely CAC, to discover frequent itemsets based on the Principle of Inclusion and Exclusion. The clustering technique groups highly similar members to improve the accuracy of support approximation. The hit ratio analysis and experimental results presented in this paper verify that CAC improves accuracy. Without repeatedly scanning a database and storing vast information in memory, the CAC method is able mine frequent itemsets with relative stability. The advantages that the CAC method enjoys in both accuracy and performance make it an effective and useful technique for discovering frequent itemsets in a database. (c) 2007 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USzh_TW
dc.relationData & Knowledge Engineeringen_US
dc.relation.ispartofseriesData & Knowledge Engineering, Volume 65, Issue 1, Page(s) 90-107.en_US
dc.relation.urihttp://dx.doi.org/10.1016/j.datak.2007.10.003en_US
dc.subjectsupport approximationen_US
dc.subjectclusteringen_US
dc.subjectdata miningen_US
dc.subjectcombinatorialen_US
dc.subjectapproximationen_US
dc.subjectfrequent itemseten_US
dc.subjectassociation rulesen_US
dc.subjectefficienten_US
dc.subjectclassificationen_US
dc.subjectalgorithmen_US
dc.titleDiscovering frequent itemsets by support approximation and itemset clusteringen_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1016/j.datak.2007.10.003zh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en_US-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.openairetypeJournal Article-
item.fulltextno fulltext-
Appears in Collections:資訊科學與工程學系所
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