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

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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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Keywords: Dunn’s index; VAT techniques; analyse de grappe; classification accuracy; cluster analysis; cluster tendency; clusters overlap; empiètement des grappes; ensemble de données pharmacologiques; indice de Dunn; pharmacological data sets; précision de la classification; techniques VAT; tendance à l’agrégation

Document Type: Research Article

Affiliations: 1: Laboratorio de Bioinformática, Centro de Estudios de Informática, Facultad de Matemática, Física y Computación, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, 54830 Villa Clara, Cuba. 2: Laboratorio de Bioinformática, Centro de Estudios de Informática, Facultad de Matemática, Física y Computación, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, 54830 Villa Clara, Cuba. 3: Laboratorio de Bioinformática, Centro de Estudios de Informática, Facultad de Matemática, Física y Computación, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, 54830 Villa Clara, Cuba. 4: Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba. 5: Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), 28049 Madrid, Spain.

Publication date: April 1, 2012

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