  The electron dynamics in the unoccupied states of the Weyl semimetal PtBi$_2$
is studied by time- and angle-resolved photoemission spectroscopy (TR-ARPES).
The measurement's result is the photoemission intensity $I$ as a function of at
least four parameters: the emission angle and kinetic energy of the
photoelectrons, the time delay between pump and probe laser pulses, and the
probe laser photon energy that needs to be varied to access the full
three-dimensional Brillouin zone of the material. The TR-ARPES results are
reported in an accompanying paper. Here we focus on the technique of using
$k$-means, an unsupervised machine learning technique, in order to discover
trends in the four-dimensional data sets. We study how to compare the electron
dynamics across the entire data set and how to reveal subtle variations between
different data sets collected in the vicinity of the bulk Weyl points.
