Cluster tendency assessment is an important stage in cluster analysis. In this sense, a group of promising techniques named VAT (Visual Assessment of Tendency) has emerged in the literature. The presence of clusters can be easily detected 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 block-like structure. In this report, the Dunn’s index is applied as a novel measure of tendency on eight pharmacological datasets, 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 clusters separability and the classification accuracy of various cluster algorithms tested on the same datasets.

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