Author: Rivera-Borroto Oscar Miguel Rabassa-Gutiérrez Mónica Grau-Ábalo Ricardo del Corazón Marrero-Ponce Yovani García-de la Vega José Manuel
Publisher: NRC Research Press
ISSN: 1205-7541
Source: Canadian Journal of Physiology and Pharmacology, Vol.90, Iss.4, 2012-04, pp. : 425-433
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
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.
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