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dc.contributorUniversitat Ramon Llull. Facultat de Turisme i Direcció Hotelera Sant Ignasi
dc.contributorUniversitat Ramon Llull. La Salle
dc.contributorInstitut de Ciències de l'Espai
dc.contributor.authorGarcía Piquer, Álvaro
dc.contributor.authorSancho Asensio, Andreu
dc.contributor.authorFornells Herrera, Albert
dc.contributor.authorGolobardes, Elisabet
dc.contributor.authorCorral Torruella, Guiomar
dc.contributor.authorTeixidó Navarro, Francesc
dc.date.accessioned2019-10-15T11:04:30Z
dc.date.accessioned2023-10-02T06:45:36Z
dc.date.available2019-10-15T11:04:30Z
dc.date.available2023-10-02T06:45:36Z
dc.date.created2015-11
dc.date.issued2019-10
dc.identifier.urihttp://hdl.handle.net/20.500.14342/3456
dc.description.abstractThe massive generation of unlabeled data of current industrial applications has attracted the interest of data mining practitioners. Thus, retrieving novel and useful information from these volumes of data while decreasing the costs of manipulating such amounts of information is a major issue. Multiobjective clustering algorithms are able to recognize patterns considering several objective function which is crucial in real-world situations. However, they dearth from a retrieval system for obtaining the most suitable solution, and due to the fact that the size of Pareto set can be unpractical for human experts, autonomous retrieval methods are fostered. This paper presents an automatic retrieval system for handling Pareto-based multiobjective clustering problems based on the shape of the Pareto set and the quality of the clusters. The proposed method is integrated in CAOS, a scalable and flexible framework, to test its performance. Our approach is compared to classic retrieval methods that only consider individual strategies by using a wide set of artificial and real-world datasets. This filtering approach is evaluated under large data volumes demonstrating its competence in clustering problems. Experiments support that the proposal overcomes the accuracy and significantly reduces the computational time of the solution retrieval achieved by the individual strategieseng
dc.format.extent33 p.
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofInformation Sciences, 2015, Vol. 320, No. 1 (November)
dc.rights© Elsevier. Tots els drets reservats
dc.sourceRECERCAT (Dipòsit de la Recerca de Catalunya)
dc.subject.otherInformàtica tova
dc.subject.otherAlgorismes genètics
dc.subject.otherSoft computing
dc.subject.otherComputer algorithms
dc.titleToward high performance solution retrieval in multiobjective clustering
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/submittedVersion
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscap
dc.subject.udc004
dc.identifier.doi10.1016/j.ins.2015.04.041


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