Linear Correlation Discovery in Databases: A Data Mining Approach

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dc.contributor.author Chiang, R en
dc.contributor.author Chua, Cecil en
dc.contributor.author Lim, E en
dc.date.accessioned 2011-11-09T19:47:43Z en
dc.date.issued 2005 en
dc.identifier.citation Data & Knowledge Engineering 53(3):311-337 2005 en
dc.identifier.issn 0169-023X en
dc.identifier.uri http://hdl.handle.net/2292/8865 en
dc.description.abstract Very little research in knowledge discovery has studied how to incorporate statistical methods to automate linear correlation discovery (LCD). We present an automatic LCD methodology that adopts statistical measurement functions to discover correlations from databases attributes. Our methodology automatically pairs attribute groups having potential linear correlations, measures the linear correlation of each pair of attribute groups, and confirms the discovered correlation. The methodology is evaluated in two sets of experiments. The results demonstrate the methodology s ability to facilitate linear correlation discovery for databases with a large amount of data en
dc.publisher Elsevier B.V. en
dc.relation.ispartofseries Data & Knowledge Engineering en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/0169-023X/ en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Linear Correlation Discovery in Databases: A Data Mining Approach en
dc.type Journal Article en
dc.identifier.doi 10.1016/j.datak.2004.09.002 en
pubs.issue 3 en
pubs.begin-page 311 en
pubs.volume 53 en
dc.rights.holder Copyright: Elsevier B.V. en
pubs.end-page 337 en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Article en
pubs.elements-id 155539 en
pubs.record-created-at-source-date 2010-10-01 en


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