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A Multi-objective Clustering Algorithm Integrating Intra-Clustering and Inter-Clustering Measures

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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.accessioned2026-03-02T15:53:03Z
dc.date.available2026-03-02T15:53:03Z
dc.date.issued2025
dc.description.abstractThis study delves into bio-inspired approaches and clustering methodologies to introduce an automated clustering algorithm named Multi-objective Clustering Algorithm (MCA). Using multi-objective strategies and several combination measures, this method calculates the optimal number of clusters and element partitioning by minimizing intra-clustering measures and maximizing inter-clustering ones. Through experimentation on three benchmark datasets, the results highlight the success of the MCA in obtaining a set of optimal solutions (Hybrid Pareto front) through the integration of multi-objective strategies and clustering measures. Moreover, the Dunn clustering validity index is used to support the decision maker in selecting the optimal solution among the ones presented in the Hybrid Pareto front. This approach allows decision-makers to choose the most suitable solution by incorporating additional insights beyond the model.eng
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 (DOI: 10.54499/UIDB/05757/2020), UIDP/05757/2020 (DOI: 10.54499/UIDB/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. (2025). A Multi-objective Clustering Algorithm Integrating Intra-Clustering and Inter-Clustering Measures. In 7th International Conference on Optimization and Learning, OLA 2024. ISSN 1865-0929. 2311, p. 97-108
dc.identifier.doi10.1007/978-3-031-77941-1_8
dc.identifier.isbn9783031779404
dc.identifier.isbn9783031779411
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttp://hdl.handle.net/10198/35918
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationALGORITMI Research Center
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofOptimization and Learning
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.subjectMulti-objective
dc.subjectBio-inspired methods
dc.subjectPartitioning-clustering
dc.titleA Multi-objective Clustering Algorithm Integrating Intra-Clustering and Inter-Clustering Measureseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/00319/2020
oaire.awardNumberUIDP/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/UIDP%2F05757%2F2020/PT
oaire.citation.endPage108
oaire.citation.startPage97
oaire.citation.title7th International Conference on Optimization and Learning, OLA 2024
oaire.citation.volume2311
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
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relation.isAuthorOfPublicatione9981d62-2a2b-4fef-b75e-c2a14b0e7846
relation.isAuthorOfPublication.latestForDiscovery04fa4023-3726-4dd5-8d97-f6b162ceb820
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