While Gamma-Ray Burst (GRBs) are clear and distinct observed events, every individual GRB
is unique. In fact, GRBs are known for their variable behaviour, and BATSE was already able to
discover two categories of GRB from the T90 distribution; the short and long GRBs. These two
categories match up with the expected two types of GRB progenitors. Nowadays, more features
have been found to be able to further distinguish them, such as the hardness ratio or the presence
of supernovae. However, that does not mean that it is by any means simple to categorise individual
GRBs. Furthermore, more GRB categories have been theorised as well, such as low-luminosity
or X-ray-rich GRBs. These different types of GRBs also could indicate a different neutrino
spectrum, with different types of GRBs more likely to emit higher amounts of neutrinos. We
present an ongoing effort to use machine learning to categorise and classify GRBs, searching for
subpopulations that could yield a larger neutrino flux. We specifically use unsupervised learning,
as it allows hidden patterns and correlations to come to light. With the help of features such as
the T90, hardness, fluence, SNR, spectral index and even the full light curve and spectra, different
structures and categories of Gamma-Ray bursts can be found.