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dc.contributor.authorChen, WCen_US
dc.contributor.authorTseng, SSen_US
dc.contributor.authorWang, CYen_US
dc.date.accessioned2014-12-08T15:39:49Z-
dc.date.available2014-12-08T15:39:49Z-
dc.date.issued2004en_US
dc.identifier.isbn3-540-22007-0en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11536/27203-
dc.description.abstractIn recent years, the procedure of manufacturing has become more and more complex. In order to meet high expectation on quality target, quick identification of root cause that makes defects is an essential issue. In this paper, we will refer to a typical algorithm of mining association rules and propose a novel interestingness measurement to provide an effective and accurate solution. First, the manufacturing defect detection problem of analyzing the correlation between combinations of machines and the result of defect is defined. Then, we propose an integrated processing procedure RMI (Root cause Machine Identifier) to discover the root cause in this problem. Finally, the results of experiments show the accuracy and efficiency of RMI are both well with real manufacturing cases.en_US
dc.language.isoen_USen_US
dc.titleA novel manufacturing defect detection method using data mining approachen_US
dc.typeArticle; Proceedings Paperen_US
dc.identifier.journalINNOVATIONS IN APPLIED ARTIFICIAL INTELLIGENCEen_US
dc.citation.volume3029en_US
dc.citation.spage77en_US
dc.citation.epage86en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000221714200009-
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