title: Dunn's index for cluster tendency assessment of pharmacological data sets.
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.
authors.affiliation: Laboratorio de Bioinformática, Centro de Estudios de Informática, Física y Computación, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Cuba. oscarrb@uclv.edu.cu
authors.forename: Oscar Miguel
authors.initials: OM
authors.lastname: Rivera-Borroto
authors.forename: Mónica
authors.initials: M
authors.lastname: Rabassa-Gutiérrez
authors.forename: Ricardo Del Corazón
authors.initials: Rdel C
authors.lastname: Grau-Ábalo
authors.forename: Yovani
authors.initials: Y
authors.lastname: Marrero-Ponce
authors.forename: José Manuel
authors.initials: JM
authors.lastname: García-de la Vega
mesh_terms: D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006801:Humans; D010600:Pharmacology / Q000706:statistics & numerical data*; D012984:Software