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dc.contributor.authorShyu, Mei-Ling
dc.contributor.authorChen, Shu-Ching
dc.contributor.authorLi, Sheng-Tun
dc.date.accessioned2009-06-02T06:20:34Z
dc.date.accessioned2020-05-25T06:36:41Z-
dc.date.available2009-06-02T06:20:34Z
dc.date.available2020-05-25T06:36:41Z-
dc.date.issued2006-10-26T01:39:40Z
dc.date.submitted2000-12-08
dc.identifier.urihttp://dspace.lib.fcu.edu.tw/handle/2377/2585-
dc.description.abstractIn this paper, we propose to incorporate domain knowl- edge in a generalized aÆnity-based association rule min- ing algorithm to reduce the size of the data so that only the potentially interesting and relevant portion of the data will be used in the computation procedure. The association rule mining algorithm is an approach to discover the quasi-equivalence relationships of the me- dia objects for databases. After the incorporation of domain knowledge, the computational performance of the mining algorithm can be improved. Experimental results are provided and analyzed.
dc.description.sponsorship中正大學,嘉義縣
dc.format.extent7p.
dc.format.extent206494 bytes
dc.format.mimetypeapplication/pdf
dc.language.isozh_TW
dc.relation.ispartofseries2000 ICS會議
dc.subject.otherData Mining & Knowledge-Based Systems
dc.titleIncorporation of Data Mining and Domain Knowledge for Discovering Quasi-Equivalence Database Relationships
分類:2000年 ICS 國際計算機會議

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