  We consider the problem related to clustering of gamma-ray bursts (from
"BATSE" catalogue) through kernel principal component analysis in which our
proposed kernel outperforms results of other competent kernels in terms of
clustering accuracy and we obtain three physically interpretable groups of
gamma-ray bursts. The effectivity of the suggested kernel in combination with
kernel principal component analysis in revealing natural clusters in noisy and
nonlinear data while reducing the dimension of the data is also explored in two
simulated data sets.
