完整後設資料紀錄
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dc.contributor.authorHu, YCen_US
dc.contributor.authorTzeng, GHen_US
dc.date.accessioned2014-12-08T15:26:04Z-
dc.date.available2014-12-08T15:26:04Z-
dc.date.issued2003en_US
dc.identifier.isbn0-9707890-2-5en_US
dc.identifier.urihttp://hdl.handle.net/11536/18479-
dc.description.abstractA fuzzy sequential pattern consisting of several fuzzy sets represents a frequently occurring behavior related to time and can be discovered from transaction bases. Actually, consumers' products preferences and consumers' product buying orders related to purchase behaviors can be found in the fuzzy sequential pattern mining. Since for each decision problem, there is a competence set for solving that problem, we consider knowledge found in fuzzy sequential pattern mining as a needed competence set for solving one decision problem. This paper uses a known competence set expansion method, the minimum spanning table method, to find appropriate two-stage learning sequences that can effectively acquire individual fuzzy knowledge sets found in the fuzzy sequential pattern mining. A numerical example is used to show the usefulness of the proposed method.en_US
dc.language.isoen_USen_US
dc.titleCompetence sets for deriving two-stage learning sequences from fuzzy knowledgeen_US
dc.typeProceedings Paperen_US
dc.identifier.journalPROCEEDINGS OF THE 7TH JOINT CONFERENCE ON INFORMATION SCIENCESen_US
dc.citation.spage443en_US
dc.citation.epage446en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000187061500112-
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