Toward high performance solution retrieval in multiobjective clustering

The 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. Multi...

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Authors: Garcia Piquer, Alvaro, Sancho-Asensio, Andreu, Fornells Herrera, Albert, Golobardes Ribé, Elisabet, Corral Torruella, Guiomar, Teixidó-Navarro, Francesc
Format: article
Status:Versión enviada para evaluación y publicación
Publication Date:2015
Country:España
Institution:Universitat Ramon Llull (URL)
Repository:DAU Arxiu Digital de la Universitat Ramon Llull
OAI Identifier:oai:dau.url.edu:20.500.14342/3456
Online Access:https://hdl.handle.net/20.500.14342/3456
https://doi.org/10.1016/j.ins.2015.04.041
Access Level:Open access
Keyword:Informàtica tova
Algorismes genètics
Soft computing
Computer algorithms
004
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oai_identifier_str oai:dau.url.edu:20.500.14342/3456
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spelling Toward high performance solution retrieval in multiobjective clusteringGarcia Piquer, AlvaroSancho-Asensio, AndreuFornells Herrera, AlbertGolobardes Ribé, ElisabetCorral Torruella, GuiomarTeixidó-Navarro, FrancescInformàtica tovaAlgorismes genèticsSoft computingComputer algorithms004The 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 strategiesElsevierUniversitat Ramon Llull. Facultat de Turisme i Direcció Hotelera Sant IgnasiUniversitat Ramon Llull. La SalleInstitut de Ciències de l'Espai201920232019202320152015info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersion33 p.application/pdfhttps://hdl.handle.net/20.500.14342/3456https://doi.org/10.1016/j.ins.2015.04.041RECERCAT (Dipòsit de la Recerca de Catalunya)reponame:DAU Arxiu Digital de la Universitat Ramon Llullinstname:Universitat Ramon Llull (URL)InglésInformation Sciences, 2015, Vol. 320, No. 1 (November)© Elsevier. Tots els drets reservatsinfo:eu-repo/semantics/openAccessoai:dau.url.edu:20.500.14342/34562026-06-21T06:40:37Z
dc.title.none.fl_str_mv Toward high performance solution retrieval in multiobjective clustering
title Toward high performance solution retrieval in multiobjective clustering
spellingShingle Toward high performance solution retrieval in multiobjective clustering
Garcia Piquer, Alvaro
Informàtica tova
Algorismes genètics
Soft computing
Computer algorithms
004
title_short Toward high performance solution retrieval in multiobjective clustering
title_full Toward high performance solution retrieval in multiobjective clustering
title_fullStr Toward high performance solution retrieval in multiobjective clustering
title_full_unstemmed Toward high performance solution retrieval in multiobjective clustering
title_sort Toward high performance solution retrieval in multiobjective clustering
dc.creator.none.fl_str_mv Garcia Piquer, Alvaro
Sancho-Asensio, Andreu
Fornells Herrera, Albert
Golobardes Ribé, Elisabet
Corral Torruella, Guiomar
Teixidó-Navarro, Francesc
author Garcia Piquer, Alvaro
author_facet Garcia Piquer, Alvaro
Sancho-Asensio, Andreu
Fornells Herrera, Albert
Golobardes Ribé, Elisabet
Corral Torruella, Guiomar
Teixidó-Navarro, Francesc
author_role author
author2 Sancho-Asensio, Andreu
Fornells Herrera, Albert
Golobardes Ribé, Elisabet
Corral Torruella, Guiomar
Teixidó-Navarro, Francesc
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universitat Ramon Llull. Facultat de Turisme i Direcció Hotelera Sant Ignasi
Universitat Ramon Llull. La Salle
Institut de Ciències de l'Espai
dc.subject.none.fl_str_mv Informàtica tova
Algorismes genètics
Soft computing
Computer algorithms
004
topic Informàtica tova
Algorismes genètics
Soft computing
Computer algorithms
004
description The 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 strategies
publishDate 2015
dc.date.none.fl_str_mv 2015
2015
2019
2019
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14342/3456
https://doi.org/10.1016/j.ins.2015.04.041
url https://hdl.handle.net/20.500.14342/3456
https://doi.org/10.1016/j.ins.2015.04.041
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Information Sciences, 2015, Vol. 320, No. 1 (November)
dc.rights.none.fl_str_mv © Elsevier. Tots els drets reservats
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © Elsevier. Tots els drets reservats
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 33 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv RECERCAT (Dipòsit de la Recerca de Catalunya)
reponame:DAU Arxiu Digital de la Universitat Ramon Llull
instname:Universitat Ramon Llull (URL)
instname_str Universitat Ramon Llull (URL)
reponame_str DAU Arxiu Digital de la Universitat Ramon Llull
collection DAU Arxiu Digital de la Universitat Ramon Llull
repository.name.fl_str_mv
repository.mail.fl_str_mv
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