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dc.date.accessioned2009-04-08T13:58:53Z
dc.date.available2009-04-08T13:58:53Z
dc.date.issued2005
dc.identifier.uriurn:nbn:de:hebis:34-2009040826965
dc.identifier.urihttp://hdl.handle.net/123456789/2009040826965
dc.format.extent223200 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subject.ddc004
dc.titleEfficient mining of association rules based on formal concept analysiseng
dc.typePreprint
dcterms.abstractAssociation rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, TITANIC, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.eng
dcterms.accessRightsopen access
dcterms.creatorLakhal, Lotfi
dcterms.creatorStumme, Gerd
dc.description.everythingAuch erschienen in: Ganter, Bernhard u.a. (Hrsg.): Formal concept analysis. (Lecutre notes in computer science ; 3626). Berlin u.a. : Springer, 2005. S. 180-195. ISBN 3-540-27891-5 (The original publication is available at www.springerlink.com)ger
dc.subject.swdFormale Begriffsanalyseger
dc.subject.swdData Miningger


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