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dc.contributor.authorDarcy, Peteren_US
dc.contributor.authorStantic, Belaen_US
dc.contributor.authorSattar, Abdulen_US
dc.contributor.editorJohann Elder, Maria Bielikova, A min Tjoaen_US
dc.date.accessioned2017-05-03T11:26:37Z
dc.date.available2017-05-03T11:26:37Z
dc.date.issued2011en_US
dc.date.modified2012-09-14T01:00:31Z
dc.identifier.issn03029743en_US
dc.identifier.doi10.1007/978-3-642-23737-9_8en_US
dc.identifier.urihttp://hdl.handle.net/10072/43326
dc.description.abstractWithin databases employed in various commercial sectors, anomalies continue to persist and hinder the overall integrity of data. Typically, Duplicate, Wrong and Missed observations of spatial-temporal data causes the user to be not able to accurately utilise recorded information. In literature, different methods have been mentioned to clean data which fall into the category of either deterministic and probabilistic approaches. However, we believe that to ensure the maximum integrity, a data cleaning methodology must have properties of both of these categories to effectively eliminate the anomalies. To realise this, we have proposed a method which relies both on integrated deterministic and probabilistic classifiers using fusion techniques. We have empirically evaluated the proposed concept with state-of-the-art techniques and found that our approach improves the integrity of the resulting data set.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent118814 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherSpringeren_US
dc.publisher.placeGermanyen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom98en_US
dc.relation.ispartofpageto110en_US
dc.relation.ispartofjournalLecture Notes in Computer scienceen_US
dc.relation.ispartofvolume6909en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchCoding and Information Theoryen_US
dc.subject.fieldofresearchNeural, Evolutionary and Fuzzy Computationen_US
dc.subject.fieldofresearchcode080401en_US
dc.subject.fieldofresearchcode080108en_US
dc.titleA Novel Integrated Classifier for Handling Data Warehouse Anomaliesen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2011 Springer Berlin / Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.comen_US
gro.date.issued2011
gro.hasfulltextFull Text


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