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dc.contributor.authorLee, Yeong-Chyi
dc.contributor.authorHong, Tzung-Pei
dc.contributor.authorLin, Wen-Yang
dc.date.accessioned2009-06-02T08:40:51Z
dc.date.accessioned2020-07-05T06:31:38Z-
dc.date.available2009-06-02T08:40:51Z
dc.date.available2020-07-05T06:31:38Z-
dc.date.issued2006-06-15T06:45:43Z
dc.date.submitted2003-12-19
dc.identifier.urihttp://dspace.fcu.edu.tw/handle/2376/1743-
dc.description.abstractData mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the previous approaches set a single minimum support threshold for all the items or itemsets. But in real applications, different items may have different criteria to judge its importance. The support requirements should then vary with different items. In this paper, we provide another point of view about defining the minimum supports of itemsets when items have different minimum supports. The maximum constraint is used, which is well explained and may be suitable to some mining domains. We then propose a simple algorithm based on the Apriori approach to find the large-itemsets and association rules under this constraint. The proposed algorithm is easy and efficient when compared to Wang et al.’s under the maximum constraint. The numbers of association rules and large itemsets obtained by the proposed mining algorithm using the maximum constraint are also less than those using the minimum constraint. Whether to adopt the proposed approach thus depends on the requirements of mining problems.
dc.description.sponsorship逢甲大學,台中市
dc.format.extent6P.
dc.format.extent217707 bytes
dc.format.mimetypeapplication/pdf
dc.language.isozh_TW
dc.relation.ispartofseries中華民國92年全國計算機會議
dc.subjectdata mining
dc.subjectmultiple minimum supports
dc.subjectassociation rules
dc.subjectmaximum constraint.
dc.subject.other其他領域
dc.titleMining Association Rules with Multiple Minimum Supports Using Maximum Constraints
分類:2003年 NCS 全國計算機會議

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