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A collaborative multi-objective approach for clustering task based on distance measures and clustering validity indices

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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorAzevedo, Beatriz Flamia
dc.contributor.authorRocha, Ana Maria A. C.
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2026-03-03T15:48:05Z
dc.date.available2026-03-03T15:48:05Z
dc.date.issued2024
dc.description.abstractClustering algorithm has the task of classifying a set of elements so that the elements within the same group are as similar as possible and, in the same way, that the elements of different groups (clusters) are as different as possible. This paper presents the Multi-objective Clustering Algorithm (MCA) combined with the NSGA-II, based on two intra- and three inter-clustering measures, combined 2-to-2, to define the optimal number of clusters and classify the elements among these clusters. As the NSGA-II is a multi-objective algorithm, the results are presented as a Pareto front in terms of the two measures considered in the objective functions. Moreover, a procedure named Cluster Collaborative Indices Procedure (CCIP) is proposed, which aims to analyze and compare the Pareto front solutions generated by different criteria (Elbow, Davies-Bouldin, Calinski-Harabasz, CS, and Dumn indices) in a collaborative way. The most appropriate solution is suggested for the decision-maker to support their final choice, considering all solutions provided by the measured combination. The methodology was tested in a benchmark dataset and also in a real dataset, and in both cases, the results were satisfactory to define the optimal number of clusters and to classify the elements of the dataset.por
dc.description.sponsorshipThis work has been supported by FCT Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020, UIDB/05757/2020, UIDP/05757/2020 and Erasmus Plus KA2 within the project 2021-1-PT01-KA220-HED-000023288. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021.
dc.identifier.citationAzevedo, Beatriz Flamia; Rocha, Ana Maria A.C.; Pereira, Ana I. (2024). A collaborative multi-objective approach for clustering task based on distance measures and clustering validity indices. In 6th International Conference on Dynamics of Information Systems. Cgam: Springer Nature. p. 54-68. ISBN 978-3-031-50320-7. DOI: 10.1007/978-3-031-50320-7_4
dc.identifier.doi10.1007/978-3-031-50320-7_4
dc.identifier.isbn978-3-031-50319-1
dc.identifier.isbn978-3-031-50320-7
dc.identifier.urihttp://hdl.handle.net/10198/35931
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationALGORITMI Research Center
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationSFRH/BD/07427/2021
dc.relation.ispartofLecture Notes in Computer Science
dc.relation.ispartofDynamics of Information Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectClustering validity índices
dc.subjectMulti-objective
dc.subjectClassification
dc.titleA collaborative multi-objective approach for clustering task based on distance measures and clustering validity indicespor
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/00319/2020
oaire.awardNumberUIDB/05757/2020
oaire.awardTitleALGORITMI Research Center
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.citation.endPage68
oaire.citation.startPage54
oaire.citation.title6th International Conference on Dynamics of Information Systems, DIS 2023
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAzevedo
person.familyNamePereira
person.givenNameBeatriz Flamia
person.givenNameAna I.
person.identifier.ciencia-id181E-855C-E62C
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.orcid0000-0002-8527-7409
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridF-3168-2010
person.identifier.scopus-author-id15071961600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication04fa4023-3726-4dd5-8d97-f6b162ceb820
relation.isAuthorOfPublicatione9981d62-2a2b-4fef-b75e-c2a14b0e7846
relation.isAuthorOfPublication.latestForDiscovery04fa4023-3726-4dd5-8d97-f6b162ceb820
relation.isProjectOfPublication0d98f999-8fd3-46a8-8a71-a7ff478a1207
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