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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. © 2018, © 2018 Taylor & Francis Group, LLC.
|Journal||Data powered by TypesetCommunications in Statistics: Simulation and Computation|
|Publisher||Data powered by TypesetTaylor and Francis Inc.|