A document clustering algorithm for discovering and describing topics

dc.contributor.author	Anaya Sánchez, Henry
dc.contributor.author	Pons Porrata, Aurora
dc.contributor.author	Berlanga Llavori, Rafael
dc.date.accessioned	2012-10-22T11:17:37Z
dc.date.available	2012-10-22T11:17:37Z
dc.date.issued	2010
dc.identifier	http://dx.doi.org/10.1016/j.patrec.2009.11.013
dc.identifier.citation	Pattern Recognition Letters, 31, 6, p. 502-510
dc.identifier.issn	1678655
dc.identifier.uri	http://hdl.handle.net/10234/49406
dc.description.abstract	In this paper, we introduce a new clustering algorithm for discovering and describing the topics comprised in a text collection. Our proposal relies on both the most probable term pairs generated from the collection and the estimation of the topic homogeneity associated to these pairs. Topics and their descriptions are generated from those term pairs whose support sets are homogeneous enough for representing collection topics. Experimental results obtained over three benchmark text collections demonstrate the effectiveness and utility of this new approach. © 2009.
dc.language.iso	eng
dc.publisher	Elsevier
dc.rights.uri	http://rightsstatements.org/vocab/CNE/1.0/	*
dc.subject	Document clustering
dc.subject	Topic description
dc.subject	Topic discovery
dc.title	A document clustering algorithm for discovering and describing topics
dc.type	info:eu-repo/semantics/article
dc.identifier.doi	http://dx.doi.org/10.1016/j.patrec.2009.11.013
dc.rights.accessRights	info:eu-repo/semantics/restrictedAccess

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