A max-min approach for hiding frequent itemsets

dc.creator	Moustakides, G. V.	en
dc.creator	Verykios, V. S.	en
dc.date.accessioned	2015-11-23T10:39:59Z
dc.date.available	2015-11-23T10:39:59Z
dc.date.issued	2006
dc.identifier.isbn	769527027
dc.identifier.issn	15504786
dc.identifier.uri	http://hdl.handle.net/11615/31168
dc.description.abstract	In this paper we are proposing a new algorithmic approach for sanitizing raw data from sensitive knowledge in the context of mining of association rules. The new approach (a) relies on the maxmin criterion which is a method in decision theory for maximizing the minimum gain and (b) builds upon the border theory of frequent itemsets. © 2006 IEEE.	en
dc.source.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-48649086773&partnerID=40&md5=7d854f2dc61e41401fe858a2087e8c9c
dc.subject	Algorithmic approach	en
dc.subject	Frequent Itemsets	en
dc.subject	Max-min	en
dc.subject	Maxmin criteria	en
dc.subject	New approaches	en
dc.subject	Associative processing	en
dc.subject	Data mining	en
dc.subject	Decision theory	en
dc.subject	Technical presentations	en
dc.title	A max-min approach for hiding frequent itemsets	en
dc.type	conferenceItem	en

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