Mostrar el registro sencillo del ítem

dc.contributorUniversitat Ramon Llull. Facultat de Turisme i Direcció Hotelera Sant Ignasi
dc.contributor.authorGarcia Piquer, Alvaro
dc.contributor.authorBacardit, Jaume
dc.contributor.authorFornells Herrera, Albert
dc.contributor.authorGolobardes, Elisabet
dc.date.accessioned2024-07-31T09:08:15Z
dc.date.available2024-07-31T09:08:15Z
dc.date.issued2016-12-02
dc.identifier.issn0167-8655ca
dc.identifier.urihttp://hdl.handle.net/20.500.14342/4378
dc.description.abstractMultiobjective evolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionary algorithms. Their capabilities allow them to find better solutions than with conventional clustering algorithms when more than one criterion is necessary to obtain understandable patterns from the data. However, these kind of techniques are expensive in terms of computational time and memory usage, and specific strategies are required to ensure their successful scalability when facing large-scale data sets. This work proposes the application of a data subset approach for scaling-up multiobjective clustering algorithms and it also analyzes the impact of three stratification methods. The experiments show that the use of the proposed data subset approach improves the performance of multiobjective evolutionary clustering algorithms without considerably penalizing the accuracy of the final clustering solution.ca
dc.format.extent10 p.ca
dc.language.isoengca
dc.publisherElsevierca
dc.relation.ispartofPattern Recognition Lettersca
dc.rights© Elsevierca
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalca
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.otherAlgorismes computacionalsca
dc.subject.otherClustering algorithmsca
dc.subject.otherEvolutionary algorithmsca
dc.subject.otherComputational timeca
dc.subject.otherFinal clusteringca
dc.subject.otherAnàlisi de conglomeratsca
dc.titleScaling-up multiobjective evolutionary clustering algorithms using stratificationca
dc.typeinfo:eu-repo/semantics/articleca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.terms24 mesosca
dc.subject.udc004ca
dc.identifier.doihttps://doi.org/10.1016/j.patrec.2016.12.001ca
dc.description.versioninfo:eu-repo/semantics/acceptedVersionca


Ficheros en el ítem

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

© Elsevier
Excepto si se señala otra cosa, la licencia del ítem se describe como http://creativecommons.org/licenses/by-nc-nd/4.0/
Compartir en TwitterCompartir en LinkedinCompartir en FacebookCompartir en TelegramCompartir en WhatsappImprimir