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dc.contributor.authorHuang, Anna
dc.contributor.authorWitten, Ian H.
dc.contributor.authorFrank, Eibe
dc.contributor.authorMilne, David N.
dc.coverage.spatialConference held at Bangkok, Thailanden_NZ
dc.date.accessioned2010-02-10T20:30:25Z
dc.date.available2010-02-10T20:30:25Z
dc.date.issued2009
dc.identifier.citationHuang, A., Witten, I. H., Frank, E. & Milne, D. (2009). Clustering documents using a Wikipedia-based concept representation. In Proceedings of 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009. (pp. 628-636).en
dc.identifier.urihttps://hdl.handle.net/10289/3559
dc.description.abstractThis paper shows how Wikipedia and the semantic knowledge it contains can be exploited for document clustering. We first create a concept-based document representation by mapping the terms and phrases within documents to their corresponding articles (or concepts) in Wikipedia. We also developed a similarity measure that evaluates the semantic relatedness between concept sets for two documents. We test the concept-based representation and the similarity measure on two standard text document datasets. Empirical results show that although further optimizations could be performed, our approach already improves upon related techniques.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren_NZ
dc.relation.urihttp://www.springerlink.com/content/fn135n77w033t619/en
dc.rightsThis is an author’s accepted version of an article published in Proceedings of 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-29. ©2009 Springer-Verlag Berlin Heidelberg.en
dc.sourcePAKDD 2009en_NZ
dc.subjectMachine learning
dc.titleClustering documents using a Wikipedia-based concept representationen
dc.identifier.doi10.1007/978-3-642-01307-2_62en
dc.relation.isPartOfProc 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Miningen_NZ
pubs.begin-page628en_NZ
pubs.elements-id18982
pubs.end-page636en_NZ
pubs.finish-date2009-04-30en_NZ
pubs.place-of-publicationGermanyen_NZ
pubs.start-date2009-04-27en_NZ
pubs.volumeLNCS 5476en_NZ


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