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Clustering Validation in the Context of Hierarchical Cluster Analysis: an Empirical Study
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Clustering Validation Affinity Coefficient Spearman Correlation Coefficient VL Methodology
Contexto Educativo
Citação
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.
Editora
Springer