Mining Dynamic Databases by Weighting

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Show simple item record Zhang, Shichao en_US Liu, Li en_US
dc.contributor.editor en_US 2010-06-18T02:06:58Z 2010-06-18T02:06:58Z 2003 en_US
dc.identifier 2003001905 en_US
dc.identifier.citation Zhang Shichao and Liu Li 2003, 'Mining Dynamic Databases by Weighting', Dept of Informatics, University of Szeged, Hungary, vol. 16, no. 1, pp. 179-205. en_US
dc.identifier.issn 0324-721X en_US
dc.identifier.other C1 en_US
dc.description.abstract A dynamic database is a set of transactions, in which the content and the size can change over time. There is an essential difference between dynamic database mining and traditional database mining. This is because recently added transactions can be more 'interesting' than those inserted long ago in a dynamic database. This paper presents a method for mining dynamic databases. This approach uses weighting techniques to increase efficiency, enabling us to reuse frequent itemsets mined previously. This model also considers the novelty of itemsets when assigning weights. In particular, this method can find a kind of new patterns from dynamic databases, referred to trend patterns. To evaluate the effectiveness and efficiency of the proposed method, we implemented our approach and compare it with existing methods. en_US
dc.language en_US
dc.publisher Dept of Informatics, University of Szeged, Hungary en_US
dc.relation.isbasedon NA en_US
dc.title Mining Dynamic Databases by Weighting en_US
dc.parent Acta Cybernetica en_US
dc.journal.volume 16 en_US
dc.journal.number 1 en_US
dc.publocation Szeged, Hungary en_US
dc.identifier.startpage 179 en_US
dc.identifier.endpage 205 en_US FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 080200 en_US
dc.personcode 020030 en_US
dc.personcode 021010 en_US
dc.percentage 100 en_US Computation Theory and Mathematics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid en_US
dc.staffid 021010 en_US

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