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dc.contributor.authorJones, Steve
dc.contributor.authorMahoui, Malika
dc.date.accessioned2008-10-13T03:59:24Z
dc.date.available2008-10-13T03:59:24Z
dc.date.issued2000-10
dc.identifier.citationJones, S. & Mahoui, M. (2000). Hierarchical document clustering using automatically extracted keyphrases. (Working paper 00/13). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1029
dc.description.abstractIn this paper we present a technique for automatically generating hierarchical clusters of documents. Our technique exploits document keyphrases as features of the document space to support clustering. In fact, we cluster keyphrases rather than documents themselves and then associate documents with keyphrase clusters. We discuss alternative measures of similarity between ‘soft-clusters’ which seed Ward’s hierarchical clustering algorithm, and present the resulting cluster hierarchies that we have produced for a large collection of scientific technical reports. We analyse the effect of the alternative similarity measures and suggest improvement to our technique.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikato, Department of Computer Scienceen_US
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.titleHierarchical document clustering using automatically extracted keyphrasesen_US
dc.typeWorking Paperen_US
uow.relation.series00/13


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