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

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

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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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