Database exploration accommodating fuzzy information퍼지 정보를 수용하는 데이타베이스 탐사 기법

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dc.contributor.advisorKim, Myoung Ho-
dc.contributor.advisor김명호-
dc.contributor.authorLee, Do-Heon-
dc.contributor.author이도헌-
dc.date.accessioned2011-12-13T05:23:34Z-
dc.date.available2011-12-13T05:23:34Z-
dc.date.issued1995-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=101792&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/33048-
dc.description학위논문(박사) - 한국과학기술원 : 전산학과, 1995.8, [ x, 118 p. ]-
dc.description.abstractDatabase exploration means activities examining the database thoroughly to acquire potentially useful information. Especially, the data mining facility discovering knowledge that is implicit, but obtainable through systematic data processing, is one of essential constituents of database exploration. The importance of data mining is emphasized, since rapidly increasing size of data makes direct exposure of raw records no longer so helpful. In addition, treatment of fuzzy information must be incorporated into database exploration facilities to cope with ubiquitous fuzziness in actual domain, and in turn, provide more effective functionality. In this thesis, we investigate database exploration techniques accommodating fuzzy information. Firstly, Level-1 Fuzzy Relational Data Model (FRDM-1) is proposed as a theoretically clear framework for processing fuzzy queries. It is hard to make a crisp query reflecting the user``s data request exactly, against a large amount of data. Fuzzy querying capability is regarded as a basic form of database exploration, since users can express their data requests with their own subjective linguistic and flexible terms. Furthermore, the ranked answer for a fuzzy query provides useful information to understand content of the database. Secondly, an interactive top-down data mining process for database summarization is devised. The process exploits fuzzy domain knowledge to hypothesize discovery targets and evaluate the validity of each hypothesis. Thirdly, the top-down data mining process is extended to discover inter-attribute relationships. Finally, the data mining process is integrated with FRDM-1 to allow more flexibility in user``s exploration request. FRDM-1 is established by two basic query languages, i.e., Level-1 Fuzzy Relational Algebra(FRA-1) and Level-1 Fuzzy Relational Calculus(FRC-1). In addition, two advanced query languages, i.e., Fuzzy Selective Relational Algebra(FSRA) and Fuzzy Selective Relational Calculus(FSRC...eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectData Mining-
dc.subjectFuzzy Database-
dc.subjectKnowledge Discovery in Databases-
dc.subject데이타베이스 지식 발견-
dc.subject데이타 채광-
dc.subject퍼지 데이타베이스-
dc.titleDatabase exploration accommodating fuzzy information-
dc.title.alternative퍼지 정보를 수용하는 데이타베이스 탐사 기법-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN101792/325007-
dc.description.department한국과학기술원 : 전산학과,-
dc.identifier.uid000925243-
dc.contributor.localauthorKim, Myoung Ho-
dc.contributor.localauthor김명호-
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CS-Theses_Ph.D.(박사논문)
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