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dc.contributor Graduate Program in Computer Engineering.
dc.contributor.advisor Gündem, Taflan.
dc.contributor.author Günay, Ufuk.
dc.date.accessioned 2023-03-16T10:06:30Z
dc.date.available 2023-03-16T10:06:30Z
dc.date.issued 2007.
dc.identifier.other CMPE 2007 G87
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12513
dc.description.abstract This study is about an endeavor towards combining association rule mining over data streams and association rule hiding for traditional databases. Mainly, a system for association rule hiding over data streams will be introduced, and related background will be developed in detail. Although there are many algorithms, some of which perform very well, developed for both association rule mining over data streams and association rule hiding for traditional databases so far, we have not meet a work on stream association rule hiding, namely a work which combines these two research areas. In this work, we introduce a new system in which we merge these two interesting research areas of association rule mining. We apply our stream association rule hiding algorithm on synthetic data. We also run our algorithm over a template guided XML data. Our performance tests show that proposed system hides association rules for data streams efficiently.
dc.format.extent 30cm.
dc.publisher Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2007.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Data mining.
dc.subject.lcsh Computer algorithms.
dc.title Association rule hiding over data streams
dc.format.pages xii, 59 leaves;


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