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dc.contributor.advisorKlein, Cerry M.eng
dc.contributor.authorSun, Wenyieng
dc.date.issued2006eng
dc.date.submitted2006 Summereng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file (viewed on September )eng
dc.descriptionVita.eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionThesis (M.S.) University of Missouri-Columbia 2006.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Industrial engineering.eng
dc.description.abstract[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This research extends the fuzzy association rule mining technique capable of processing quantitative data and implements it to study the relationships among state social, political context, entrepreneurial activities, and economic development. The 5-cluster method is compared with 3-cluster method to study the effect of the number of clusters to this problem. Also, this research conducts a sensitivity analysis to study the effect of minimum support and confidence. The results of the analysis reveal that the 5-cluster method is necessary and the minimum support "0.05" and the minimum confidence "0.8" are chosen for the problem. Additionally, the marginal effect of the premises of a rule is generated automatically along with a set of interesting rules revealing the cause and effect of entrepreneurial activities. In economics, the improved fuzzy association rule mining methodology can be used as a complement of conventional econometric tools to help discovering previously hidden but potentially interesting economic relationships and generating ideas for new theories. In addition, it can be applied to various research and business fields to conduct quantitative data analysis.eng
dc.identifier.merlinb5948942xeng
dc.identifier.oclc171290700eng
dc.identifier.urihttps://hdl.handle.net/10355/5869
dc.identifier.urihttps://doi.org/10.32469/10355/5869eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsAccess is limited to the campus of the University of Missouri--Columbia.eng
dc.subject.lcshData miningeng
dc.subject.lcshEconomic developmenteng
dc.titleData mining extension for economicseng
dc.typeThesiseng
thesis.degree.disciplineIndustrial and manufacturing systems engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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