  The widespread availability of large amounts of genomic data on the
SARS-CoV-2 virus, as a result of the COVID-19 pandemic, has created an
opportunity for researchers to analyze the disease at a level of detail unlike
any virus before it. One one had, this will help biologists, policy makers and
other authorities to make timely and appropriate decisions to control the
spread of the coronavirus. On the other hand, such studies will help to more
effectively deal with any possible future pandemic. Since the SARS-CoV-2 virus
contains different variants, each of them having different mutations,
performing any analysis on such data becomes a difficult task. It is well known
that much of the variation in the SARS-CoV-2 genome happens disproportionately
in the spike region of the genome sequence -- the relatively short region which
codes for the spike protein(s). Hence, in this paper, we propose an approach to
cluster spike protein sequences in order to study the behavior of different
known variants that are increasing at very high rate throughout the world. We
use a k-mers based approach to first generate a fixed-length feature vector
representation for the spike sequences. We then show that with the appropriate
feature selection, we can efficiently and effectively cluster the spike
sequences based on the different variants. Using a publicly available set of
SARS-CoV-2 spike sequences, we perform clustering of these sequences using both
hard and soft clustering methods and show that with our feature selection
methods, we can achieve higher F1 scores for the clusters.
