  In searching for continuous gravitational waves over very many ($\approx
10^{17}$) templates , clustering is a powerful tool which increases the search
sensitivity by identifying and bundling together candidates that are due to the
same root cause. We implement a deep learning network that identifies clusters
of signal candidates in the output of continuous gravitational wave searches
and assess its performance. For loud signals our network achieves a detection
efficiency higher than 97\% with a very low false alarm rate, and maintains a
reasonable detection efficiency for signals with lower amplitudes, i.e. at
$\lesssim$ current upper limit values.
