Dunn's index for cluster tendency assessment of pharmacological data sets

Oscar Miguel Rivera-Borroto, Mónica Rabassa-Gutiérrez, Ricardo del Corazón Grau-Ábalo, Yovani Marrero-Ponce, José Manuel García de la Vega

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

16 Citas (Scopus)

Resumen

Cluster tendency assessment is an important stage in cluster analysis. In this sense, a group of promising techniques named visual assessment of tendency (VAT) has emerged in the literature. The presence of clusters can be detected easily through the direct observation of a dark blocks structure along the main diagonal of the intensity image. Alternatively, if the Dunn's index for a single linkage partition is greater than 1, then it is a good indication of the blocklike structure. In this report, the Dunn's index is applied as a novel measure of tendency on 8 pharmacological data sets, represented by machine- learning-selected molecular descriptors. In all cases, observed values are less than 1, thus indicating a weak tendency for data to form compact clusters. Other results suggest that there is an increasing relationship between the Dunn's index as a measure of cluster separability and the classification accuracy of various cluster algorithms tested on the same data sets.

Idioma originalInglés
Páginas (desde-hasta)425-433
Número de páginas9
PublicaciónCanadian Journal of Physiology and Pharmacology
Volumen90
N.º4
DOI
EstadoPublicada - abr. 2012
Publicado de forma externa

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