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dc.contributor.authorSaha, Budhaditya
dc.contributor.authorLazarescu, Mihai
dc.contributor.authorVenkatesh, Svetha
dc.contributor.editorIEEE computer society
dc.date.accessioned2017-01-30T11:06:33Z
dc.date.available2017-01-30T11:06:33Z
dc.date.created2014-10-28T02:23:21Z
dc.date.issued2007
dc.identifier.citationSaha, B. and Lazarescu, M. and Venkatesh, S. 2007. Infrequent Item mining in multiple data streams, in Seventh IEEE International Conference on Data mining, ICDM workshops, Oct 28-31 2007, pp. 569-574. Omaha, USA: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/8427
dc.identifier.doi10.1109/ICDMW.2007.32
dc.description.abstract

The problem of extracting infrequent patterns from streams and building associations between these patterns is becoming increasingly relevant today as many events of interest such as attacks in network data or unusual stories in news data occur rarely. The complexity of the problem is compounded when a system is required to deal with data from multiple streams. To address these problems, we present a framework that combines the time based association mining with a pyramidal structure that allows a rolling analysis of the stream and maintains a synopsis of the data without requiring increasing memory resources. We apply the algorithms and show the usefulness of the techniques.

dc.publisherIEEE computer society publishing services
dc.titleInfrequent Item mining in multiple data streams
dc.typeConference Paper
dcterms.source.startPage569
dcterms.source.endPage574
dcterms.source.titleProceedings of the 7th IEEE International conference on Data mining and associated workshops
dcterms.source.seriesProceedings of the 7th IEEE International conference on Data mining and associated workshops
dcterms.source.isbn0769530192
dcterms.source.conference2nd international workshop on data stream mining and management 2007 in conjunction with the 2007 International conference on Data mining
dcterms.source.conference-start-dateOct 28 2007
dcterms.source.conferencelocationOmaha, USA
dcterms.source.placeUSA
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available


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