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dc.contributorUniversitat Ramon Llull. Facultat de Turisme i Direcció Hotelera Sant Ignasi
dc.contributorUniversitat Ramon Llull. La Salle
dc.contributorInstitut d'Estudis Espacials de Catalunya
dc.contributorNewcastle University. School of Computing Science
dc.creatorGarcia-Piquer, Alvaro
dc.creatorBacardit, Jaume
dc.creatorFornells Herrera, Albert
dc.creatorGolobardes, Elisabet
dc.date.accessioned2019-10-29T11:08:24Z
dc.date.accessioned2024-07-19T07:42:52Z
dc.date.available2019-10-29T11:08:24Z
dc.date.available2024-07-19T07:42:52Z
dc.date.created2017-07
dc.date.issued2019-10
dc.identifier.urihttp://hdl.handle.net/20.500.14342/4324
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.eng
dc.format.extent18 p.
dc.publisherElsevier
dc.relation.ispartofPattern Recognition Letter, 2017, Vol. 93, Issue 1 (July)
dc.rightsL'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons:http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights© Elsevier
dc.sourceRECERCAT (Dipòsit de la Recerca de Catalunya)
dc.titleScaling-up multiobjective evolutionary clustering algorithms using stratification
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion


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