Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/481805
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DC FieldValueLanguage
dc.coverage.spatialComputer Science and Engineering
dc.date.accessioned2023-05-09T06:17:10Z-
dc.date.available2023-05-09T06:17:10Z-
dc.identifier.urihttp://hdl.handle.net/10603/481805-
dc.description.abstractDue to the advancement of the data storage and processing capabilities of computers most of the real life applications are shifted to digital domains and many of them are data intensive In general most of the applications deal with similar type of data items but due to variety of reasons some data points are present in the data set which are deviating from the normal behaviors of common data points Such type of data points are referred as outliers and in general the number of outliers in a
dc.format.extentNot Available
dc.languageEnglish
dc.relationNot Available
dc.rightsself
dc.titleData pruning based outlier detection
dc.title.alternativeNot available
dc.creator.researcherPamula, Rajendra
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordEngineering and Technology
dc.description.noteNot Available
dc.contributor.guideNandi, Sukumar and Deka, Jatindra Kr
dc.publisher.placeGuwahati
dc.publisher.universityIndian Institute of Technology Guwahati
dc.publisher.institutionDepartment of Computer Science and Engineering
dc.date.registered2005
dc.date.completed2015
dc.date.awarded2015
dc.format.dimensionsNot Available
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science and Engineering

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