Clustering Validation in the Context of  Hierarchical Cluster Analysis: an Empirical Study

Silva, Osvaldo Dias Lopes da; Sousa, Áurea; Bacelar-Nicolau, Helena

Publication

Clustering Validation in the Context of Hierarchical Cluster Analysis: an Empirical Study

2023Book part

dc.contributor.author	Silva, Osvaldo Dias Lopes da
dc.contributor.author	Sousa, Áurea
dc.contributor.author	Bacelar-Nicolau, Helena
dc.date.accessioned	2025-02-06T10:26:39Z
dc.date.available	2025-02-06T10:26:39Z
dc.date.issued	2023
dc.description.abstract	ABSTRACT: The evaluation of clustering structures is a crucial step in cluster analysis. This study presents the main results of the hierarchical cluster analysis of variables concerning a real dataset in the context of Higher Education. The goal of this research is to find a typology of some relevant items taking into account both the homogeneity and the isolation of the clusters.Two similarity measures, namely the standard affinity coefficient and Spearman’s correlation coefficient, were used, and combined with three probabilistic (AVL, AVB and AV1) aggregation criteria, from a parametric family in the scope of the VL (Validity Link) methodology. The best partitions were selected based on some validation indices, namely the global STAT levels statistics and the measures P(I2, Σ) and , adapted to the case of similarity coefficients. In order to evaluate the clusters and identify their most representative elements, the Mann and Whitney U statistics and the silhouette plot were also used.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Silva, O., Sousa, Á., & Bacelar-Nicolau, H. (2023). Clustering validation in the context of hierarchical cluster analysis: an empirical study. In P. Brito, J. G. Dias, B. Lausen, A. Montanari & R. Nugent (Eds.), Classification and data science in the digital age, pp. 343-351. Springer.	pt_PT
dc.identifier.doi	https://doi.org/10.1007/978-3-031-09034-9_37	pt_PT
dc.identifier.isbn	978-3-031-09034-9
dc.identifier.uri	http://hdl.handle.net/10400.3/7271
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Springer	pt_PT
dc.relation	This work is financed by national funds through FCT– Founda tion for Science and Technology, I.P., within the scope of the project «UIDB/04647/2020» of CICS.NOVA– Centro de Ciências Sociais da Universidade Nova de Lisboa.	pt_PT
dc.relation.publisherversion	https://link.springer.com/book/10.1007/978-3-031-09034-9	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Clustering Validation	pt_PT
dc.subject	Affinity Coefficient	pt_PT
dc.subject	Spearman Correlation Coefficient	pt_PT
dc.subject	VL Methodology	pt_PT
dc.title	Clustering Validation in the Context of Hierarchical Cluster Analysis: an Empirical Study	pt_PT
dc.type	book part
dspace.entity.type	Publication
oaire.citation.endPage	351	pt_PT
oaire.citation.startPage	343	pt_PT
oaire.citation.title	https://link.springer.com/book/10.1007/978-3-031-09034-9	pt_PT
oaire.citation.volume	Studies in Classification, Data Analysis, and Knowledge Organization - Classification and Data Science in the Digital Age	pt_PT
person.familyName	Silva
person.familyName	Sousa
person.familyName	Bacelar-Nicolau
person.givenName	Osvaldo Dias Lopes
person.givenName	Áurea
person.givenName	Helena
person.identifier	1242931
person.identifier.ciencia-id	BB18-ED20-169F
person.identifier.ciencia-id	F918-C1BD-E8CE
person.identifier.orcid	0000-0002-0269-8153
person.identifier.orcid	0000-0003-3151-5237
person.identifier.orcid	0000-0001-9663-3977
person.identifier.rid	AAD-3290-2019
person.identifier.scopus-author-id	35184112600
person.identifier.scopus-author-id	26533966300
rcaap.rights	openAccess	pt_PT
rcaap.type	bookPart	pt_PT
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