Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11982
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dc.contributor.authorChen, G.-
dc.contributor.authorWei, Q.-
dc.contributor.authorKerre, E.-
dc.contributor.authorWETS, Geert-
dc.date.accessioned2011-06-01T11:53:28Z-
dc.date.availableNO_RESTRICTION-
dc.date.available2011-06-01T11:53:28Z-
dc.date.issued2003-
dc.identifier.citationProceedings ISIS 2003.-
dc.identifier.urihttp://hdl.handle.net/1942/11982-
dc.description.abstractAssociations, as specific forms of knowledge, reflect relationships among items in databases, and have been widely studied in the fields of knowledge discovery and data mining. Recent years have witnessed many efforts on discovering fuzzy associations, aimed at coping with fuzziness in knowledge representation and decision support processes. This paper focuses on associations of three kinds, namely, association rules, functional dependencies and pattern associations, and overviews major fuzzy logic extensions accordingly.-
dc.language.isoen-
dc.titleOverview of fuzzy associations mining-
dc.typeProceedings Paper-
local.bibliographicCitation.conferencenameInternational Symposium on Advanced Intelligent Systems-
local.bibliographicCitation.conferenceplaceJeju, Korea, 1/09/2003-
local.bibliographicCitation.jcatC2-
local.type.specifiedProceedings Paper-
dc.bibliographicCitation.oldjcatC3-
local.bibliographicCitation.btitleProceedings ISIS 2003-
item.contributorChen, G.-
item.contributorWei, Q.-
item.contributorKerre, E.-
item.contributorWETS, Geert-
item.accessRightsClosed Access-
item.fullcitationChen, G.; Wei, Q.; Kerre, E. & WETS, Geert (2003) Overview of fuzzy associations mining. In: Proceedings ISIS 2003..-
item.fulltextWith Fulltext-
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