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dc.contributorUniversitat Ramon Llull. La Salle
dc.contributor.authorSevillano Domínguez, Xavier
dc.contributor.authorCobo Rodríguez, Germán
dc.contributor.authorAlías Pujol, Francesc
dc.contributor.authorSocoró Carrié, Joan Claudi
dc.date.accessioned2021-06-01T19:50:00Z
dc.date.accessioned2023-07-13T09:53:08Z
dc.date.available2021-06-01T19:50:00Z
dc.date.available2023-07-13T09:53:08Z
dc.date.created2006-08
dc.date.issued2006-08
dc.identifier.urihttp://hdl.handle.net/20.500.14342/2971
dc.description.abstractThe performance of document clustering systems depends on employing optimal text representations, which are not only difficult to determine beforehand, but also may vary from one clustering problem to another. As a first step towards building robust document clusterers, a strategy based on feature diversity and cluster ensembles is presented in this work. Experiments conducted on a binary clustering problem show that our method is robust to near-optimal model order selection and able to detect constructive interactions between different document representations in the test bed.eng
dc.format.extent2 p.cat
dc.language.isoengcat
dc.publisher29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, 6-11 of August 2006cat
dc.rights© Association for Computing Machinery. Tots els drets reservats
dc.sourceRECERCAT (Dipòsit de la Recerca de Catalunya)
dc.subject.otherIntel·ligència artificial -- Aplicacions a l'enginyeriacat
dc.subject.otherAlgorismescat
dc.titleFeature Diversity in Cluster Ensembles for Robust Document Clusteringcat
dc.typeinfo:eu-repo/semantics/conferenceObjectcat
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapcat
dc.subject.udc004
dc.subject.udc62


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