  Fast Radio Bursts (FRBs) are mysterious bursts in the millisecond timescale
at radio wavelengths. Currently, there is little understanding about the
classification of repeating FRBs, based on difference in physics, which is of
great importance in understanding their origin. Recent works from the
literature focus on using specific parameters to classify FRBs to draw
inferences on the possible physical mechanisms or properties of these FRB
subtypes. In this study, we use publicly available 1652 repeating FRBs from
FRB121102 detected with the Five-hundred-meter Aperture Spherical Telescope
(FAST), and studied them with an unsupervised machine learning model. By
fine-tuning the hyperparameters of the model, we found that there is an
indication for four clusters from the bursts of FRB121102 instead of the two
clusters ("Classical" and "Atypical") suggested in the literature. Wherein, the
"Atypical" cluster can be further classified into three sub-clusters with
distinct characteristics. Our findings show that the clustering result we
obtained is more comprehensive not only because our study produced results
which are consistent with those in the literature but also because our work
uses more physical parameters to create these clusters. Overall, our methods
and analyses produced a more holistic approach in clustering the repeating FRBs
of FRB121102.
