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A Two-Step Classification Approach to Unsupervised Record Linkage

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dc.contributor.authorChristen, Peter
dc.date.accessioned2015-12-08T22:30:02Z
dc.date.issued2007
dc.date.updated2015-12-08T09:27:07Z
dc.description.abstractLinking or matching databases is becoming increasingly important in many data mining projects, as linked data can contain information that is not available otherwise, or that would be too expensive to collect manually. A main challenge when linking large
dc.identifier.issn1445-1336
dc.identifier.urihttp://hdl.handle.net/1885/34333
dc.publisherAustralian Computer Society Inc.
dc.sourceConferences in Research and Practice in Information Technology - CRPIT
dc.subjectKeywords: Clustering; Data linkage; Data matching; Deduplication; Quality measures; Data handling; Data mining; Learning systems; Support vector machines; Classification (of information) Clustering; Data linkage; Data matching; Deduplication; Entity resolution; Quality measures; Support vector machines
dc.titleA Two-Step Classification Approach to Unsupervised Record Linkage
dc.typeJournal article
local.bibliographicCitation.lastpage119
local.bibliographicCitation.startpage111
local.contributor.affiliationChristen, Peter, College of Engineering and Computer Science, ANU
local.contributor.authoruidChristen, Peter, u4021539
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationU3594520xPUB112
local.identifier.citationvolume70
local.identifier.scopusID2-s2.0-44649135932
local.type.statusPublished Version

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