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dc.date.accessioned2009-02-23T15:14:34Z
dc.date.available2009-02-23T15:14:34Z
dc.date.issued2000
dc.identifier.uriurn:nbn:de:hebis:34-2009022326363
dc.identifier.urihttp://hdl.handle.net/123456789/2009022326363
dc.format.extent167844 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subject.ddc004
dc.titleConceptual knowledge discovery and data analysiseng
dc.typeAufsatz
dcterms.abstractIn this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin.eng
dcterms.accessRightsopen access
dcterms.creatorHereth, Joachim
dcterms.creatorStumme, Gerd
dcterms.creatorWille, Rudolf
dcterms.creatorWille, Uta
dc.description.etExternger
dc.description.everythingAuch erschienen in: Ganter, Bernhard u.a. (Hrsg.): Conceptual structures. (Lecture notes in computer science ; 1867). Berlin u.a. : Springer, 2000. S. 421-437. ISBN 3-540-67859-X (The original publication is available at www.springerlink.com)ger
dc.subject.swdWissenserwerbger
dc.subject.swdFormale Begriffsanalyseger


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