EDUA: An efficient algorithm for dynamic database mining

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Show simple item record Zhang, Shichao en_US Zhang, Jilian en_US Zhang, Chengqi en_US 2009-12-21T02:31:44Z 2009-12-21T02:31:44Z 2007 en_US
dc.identifier 2007000686 en_US
dc.identifier.citation Zhang Shichao, Zhang Jilian, and Zhang Chengqi 2007, 'EDUA: An efficient algorithm for dynamic database mining', Elsevier, vol. 177, no. 13, pp. 2756-2767. en_US
dc.identifier.issn 0020-0255 en_US
dc.identifier.other C1 en_US
dc.description.abstract Maintaining frequent itemsets (patterns) is one of the most important issues faced by the data mining community. While many algorithms for pattern discovery have been developed, relatively little work has been reported on mining dynamic databases, a major area of application in this field. In this paper, a new algorithm, namely the Efficient Dynamic Database Updating Algorithm (EDUA), is designed for mining dynamic databases. It works well when data deletion is carried out in any subset of a database that is partitioned according to the arrival time of the data. A pruning technique is proposed for improving the efficiency of the EDUA algorithm. Extensive experiments are conducted to evaluate the proposed approach and it is demonstrated that the EDUA is efficient. en_US
dc.publisher Elsevier en_US
dc.relation.isbasedon en_US
dc.title EDUA: An efficient algorithm for dynamic database mining en_US
dc.parent Information Sciences en_US
dc.journal.volume 177 en_US
dc.journal.number 13 en_US
dc.publocation Netherlands en_US
dc.identifier.startpage 2756 en_US
dc.identifier.endpage 2767 en_US QCIS Investment Core en_US
dc.conference Verified OK en_US
dc.for 080604 en_US
dc.personcode 020030 en_US
dc.personcode 0000036147 en_US
dc.personcode 011221 en_US
dc.percentage 100 en_US Database Management en_US
dc.classification.type FOR-08 en_US
dc.description.keywords Dynamic database mining; Pattern maintenance; EDUA; Scan reduction strategy en_US
dc.staffid 011221 en_US

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