A multi-level conceptual data reduction approach based on the Lukasiewicz implication

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A multi-level conceptual data reduction approach based on the Lukasiewicz implication

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dc.contributor.author Elloumi, Samir
dc.contributor.author Jaam, Jihad
dc.contributor.author Hasnah, Ahmad
dc.contributor.author Jaoua, Ali
dc.contributor.author Nafkha, Ibtissem
dc.date.accessioned 2009-12-30T07:40:05Z
dc.date.available 2009-12-30T07:40:05Z
dc.date.issued 2003-06-20
dc.identifier.citation Volume 163, Issue 4, 18 June 2004, Pages 253-262 en_US
dc.identifier.uri http://dx.doi.org/10.1016/j.ins.2003.06.013
dc.identifier.uri http://hdl.handle.net/10576/10582
dc.description.abstract Starting from fuzzy binary data represented as tables in the fuzzy relational database, in this paper, we use fuzzy formal concept analysis to reduce the tables size to only keep the minimal rows in each table, without losing knowledge (i.e., association rules extracted from reduced databases are identical at given precision level). More specifically, we develop a fuzzy extension of a previously proposed algorithm for crisp data reduction without loss of knowledge. The fuzzy Galois connection based on the Lukasiewicz implication is mainly used in the definition of the closure operator according to a precision level, which makes data reduction sensitive to the variation of this precision level. en_US
dc.language.iso en en_US
dc.publisher Elsevier Science Inc en_US
dc.subject Fuzzy data reduction en_US
dc.subject Lukasiewicz implication en_US
dc.subject Fuzzy Galois connection en_US
dc.subject Precision level en_US
dc.title A multi-level conceptual data reduction approach based on the Lukasiewicz implication en_US
dc.type Article en_US

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