  Fast radio bursts (FRBs) are one of the most mysterious astronomical
transients. Observationally, they can be classified into repeaters and
apparently non-repeaters. However, due to the lack of continuous observations,
some apparently repeaters may have been incorrectly recognized as
non-repeaters. In a series of two papers, we intend to solve such problem with
machine learning. In this second paper of the series, we focus on an array of
unsupervised machine learning methods. We apply multiple unsupervised machine
learning algorithms to the first CHIME/FRB catalog to learn their features and
classify FRBs into different clusters without any premise about the FRBs being
repeaters or non-repeaters. These clusters reveal the differences between
repeaters and non-repeaters. Then, by comparing with the identities of the FRBs
in the observed classes, we evaluate the performance of various algorithms and
analyze the physical meaning behind the results. Finally, we recommend a list
of most credible repeater candidates as targets for future observing campaigns
to search for repeated bursts in combination of the results presented in Paper
I using supervised machine learning methods.
