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tokens: very well separated components () and with marginal distributions and pairwise scatter plots as shown in the diagonal and lower triangle of the matrix of plots in Fig. 3(a) . The upper triangle of the matrix displays the correlation between each pair of variables. Thus, the first two of the three variables have a very high (almost linear) pairwise correlation of 0.98, while the other two pairs have little to modest correlations between them. It is tempting to surmise that one of the first two dimensions are nearly redundant and that dropping one of them would not have much effect on the quality of clustering. We test this assertion by performing GMMBC (with BIC used to determine the optimal number of groups) on the dataset using each of the three distinct pairs of variables. Figs 3(b) –(d) display the results of clustering each pair of coordinates, with colours representing the obtained clustering and character denoting the true grouping. Including either one of the two seemingly redundant variables with the third variable identifies only two groups and a partitioning (Figs 3b and c ) that hardly matches the true with an adjusted Rand index of 0.37. Interestingly, clustering the two seemingly redundant variables (and ignoring the third) does better, identifying four groups and with . However, this is still a far cry from the perfect partitioning that is obtained when all three coordinates are used in GMMBC with BIC to determine the optimal number of groups. Therefore, it is important to consider the relevant variables in clustering. This fact becomes more important in the light of attempts by many researchers to cluster GRBs using, for instance, only the duration variables. This example also demonstrates that two variables having correlations even as high as 0.98 are not necessarily redundant and should be carefully analysed before arriving at decisions on their inclusion or exclusion in analysis. Raftery Dean ( 2006 ) proposed a method for selecting variables containing the most relevant clustering information by recasting the variable selection problem in terms of model selection where comparison of the models is done via BIC (refer to Chattopadhyay Maitra 2017 for a thorough review on variable selection). Figure 3. Result of clustering (a) data simulated from a three-dimensional five-component GMM , using (b, c, d) two and (e) all three variables. 2.3 Measuring distinctiveness of partitioning through the overlap Chattopadhyay Maitra ( 2017 ) explain that an overlap measure can be used to indicate the extent to which clusters obtained through a method are distinct from one another. We refer to that paper for further details, but note that they adopt Maitra Melnykov ( 2010 )’s definition of the pairwise overlap between two groups as the sum of their misclassification probabilities. To provide a sense of these pairwise overlap measures, we illustrate three two-dimensional examples in Fig. 4 . (For ease of display and understanding, we use two