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Clustering methods are an important tool to enumerate and describe thedifferent coherent kinds of Gamma Ray Bursts (GRBs). But their performance canbe affected by a number of factors such as the choice of clustering algorithmand inherent associated assumptions, the inclusion of variables in clustering,nature of initialization methods used or the iterative algorithm or thecriterion used to judge the optimal number of groups supported by the data. Weanalyzed GRBs from the BATSE 4Br catalog using $k$-means and Gaussian MixtureModels-based clustering methods and found that after accounting for all theabove factors, all six variables -- different subsets of which have been usedin the literature -- and that are, namely, the flux duration variables($T_{50}$, $T_{90}$), the peak flux ($P_{256}$) measured in 256-millisecondbins, the total fluence ($F_t$) and the spectral hardness ratios ($H_{32}$ and$H_{321}$) contain information on clustering. Further, our analysis foundevidence of five different kinds of GRBs and that these groups have differentkinds of dispersions in terms of shape, size and orientation. In terms ofduration, fluence and spectrum, the five types of GRBs were characterized asintermediate/faint/intermediate, long/intermediate/soft,intermediate/intermediate/intermediate, short/faint/hard andlong/bright/intermediate.

Gaussian-Mixture-Model-based Cluster Analysis Finds Five Kinds of Gamma Ray Bursts in the BATSE Catalog