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dc.contributor.authorTsai, S.M.
dc.contributor.authorChen, Chien-Ming
dc.date.accessioned2009-06-02T07:20:25Z
dc.date.accessioned2020-05-29T06:18:56Z-
dc.date.available2009-06-02T07:20:25Z
dc.date.available2020-05-29T06:18:56Z-
dc.date.issued2006-10-27T07:30:22Z
dc.date.submitted1999-12-20
dc.identifier.urihttp://dspace.fcu.edu.tw/handle/2377/2753-
dc.description.abstractIn this paper, we examine the issue of mining quantitative association rules in a large database of sales transactions. When purchased quantities are considered, Most of the supports for items associated with their purchased quantities may be low, and the number of potentially interesting association rules discovered may be few. In order to discover more potentially interesting rules, we present two partition algorithms to partition all the possible quantities into intervals for each item. We also propose an efficient mechanism to discover all the large itemsets form the partition result. Experimental results show that by our approach, the total execution time can be reduced significantly. Moreover, the number of potentially interesting association rules discovered from our partition result is larger than that of rules discovered from the original data, which demonstrates the significance of our work.
dc.description.sponsorship淡江大學, 台北縣
dc.format.extent8p.
dc.format.extent1027974 bytes
dc.format.mimetypeapplication/pdf
dc.language.isozh_TW
dc.relation.ispartofseries1999 NCS會議
dc.subject.otherData Warehouse
dc.titleDiscovery of Quantitative Association Rules from a Large Database of Sales Transactions
分類:1999年 NCS 全國計算機會議

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