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Classification of postal distribution centres

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datacite.subject.fosCiências Naturais::Matemáticas
dc.contributor.authorAleixo, Sandra M.
dc.contributor.authorFernandes, Célia
dc.contributor.authorRamos, Paulo
dc.contributor.authorRamires, Margarida
dc.contributor.authorMilho, Rute
dc.contributor.authorAleixo, Sandra
dc.contributor.authorFernandes, Célia
dc.contributor.authorRamos, Paulo
dc.contributor.editorSpringer
dc.date.accessioned2026-10-14T14:35:19Z
dc.date.available2026-10-14T14:35:19Z
dc.date.issued2026
dc.description.abstractThis work was conducted in the context of industrial mathematics, in partnership with CTT-Correios de Portugal, S.A., highlighting the strong link between academia and the business world. It addresses the classification of Postal Distribution Centres using multivariate statistical methods combined in a less common way. First, a correlation analysis was conducted on the variables selected for the study, revealing several statistically significant associations. To mitigate collinearity, Principal Component Analysis was applied to derive uncorrelated components, which were then used as inputs to Cluster Analysis. Both hierarchical and partitional clustering methods were applied, and solution quality was assessed using graphical inspection, silhouette coefficients, and within- and between-cluster sums of squares. Ward’s method, k-means, and k-medoids produced consistent and similar groupings; in this paper, the k-means results are considered. Linear Discriminant Analysis and Multinomial Logistic Regression were used to assign new Postal Distribution Centres to the previously identified groups. The second classifier was used to corroborate the classification results obtained by LDA, since some LDA assumptions are violated and one of the clusters is small. The classification results from both methods showed a high degree of agreement. To avoid inflated accuracy caused by using the same data throughout all stages of the pipeline (standardisation, PCA, CA, LDA/MLR), the leave-one-out cross-validation method was applied across the entire pipeline, rather than only at the classification stage. As a result, the accuracy value obtained is a more realistic estimate of the model’s performance. All analyses were conducted in R.eng
dc.identifier.citationAleixo, S. M., Fernandes, C., Ramos, P., Ramires, M., & Milho, R. (2027). Classification of postal distribution centres. In O. Gervasi et al. (Eds.), Computational science and its applications – ICCSA 2026 workshops (Lecture Notes in Computer Science, Vol. 16759). Springer. https://doi.org/10.1007/978-3-032-30518-3_15
dc.identifier.doi10.1007/978-3-032-30518-3_15
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10400.21/23224
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationUID/00006/2025; UID/PRR/00006/2025
dc.rights.uriN/A
dc.subjectIndustrial mathematics
dc.subjectPostal distribution centres
dc.subjectPrincipal component analysis
dc.subjectCluster analysis
dc.subjectLinear discriminant analysis
dc.subjectMultinomial logistic regression
dc.subjectCross-validation
dc.titleClassification of postal distribution centreseng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2026
oaire.citation.conferencePlaceBraga
oaire.citation.titleComputational Science and Its Applications – ICCSA 2026 Workshops
oaire.citation.volume16759
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAleixo
person.familyNameFernandes
person.familyNameRamos
person.givenNameSandra
person.givenNameCélia
person.givenNamePaulo
person.identifier.orcid0000-0003-1740-8371
person.identifier.orcid0000-0002-2298-9863
person.identifier.orcid0000-0003-1721-3355
person.identifier.scopus-author-id24829443600
person.identifier.scopus-author-id54402521000
person.identifier.scopus-author-id54403488900
relation.isAuthorOfPublicationc7cbebb9-e51f-4f99-92cd-4b3eee61c6c9
relation.isAuthorOfPublicationf5e2c5b8-159f-4e88-8cfa-ce8227abb267
relation.isAuthorOfPublicationf91ecdf9-54df-4e6d-becf-31df89d6bb52
relation.isAuthorOfPublication.latestForDiscoveryc7cbebb9-e51f-4f99-92cd-4b3eee61c6c9

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