  The morphology of a collision cascade is an important aspect in understanding
the formation of defects and their distribution. While the number of
subcascades is an essential parameter to describe the cascade morphology, the
methods to compute this parameter are limited. We present a method to compute
the number of subcascades from the primary damage state of the collision
cascade. Existing methods analyse peak damage state or the end of ballistic
phase to compute the number of subcascades which is not always available in
collision cascade databases. We use density based clustering algorithm from
unsupervised machine learning domain to identify the subcascades from the
primary damage state. To validate the results of our method we first carry out
a parameter sensitivity study of the existing algorithms. The study shows that
the results are sensitive to input parameters and the choice of the time-frame
analyzed. On a database of 100 collision cascades in W, we show that the method
we propose, which analyzes primary damage state to predict number of
subcascades, is in good agreement with the existing method that works on the
peak state. We also show that the number of subcascades found with different
parameters can be used to classify and group together the cascades that have
similar time-evolution and fragmentation.
