  Fast radio transient search algorithms identify signals of interest by
iterating and applying a threshold on a set of matched filters. These filters
are defined by properties of the transient such as time and dispersion. A real
transient can trigger hundreds of search trials, each of which has to be
post-processed for visualization and classification tasks. In this paper, we
have explored a range of unsupervised clustering algorithms to cluster these
redundant candidate detections. We demonstrate this for Realfast, the commensal
fast transient search system at the Very Large Array. We use four features for
clustering: sky position (l, m), time and dispersion measure (DM). We develop a
custom performance metric that makes sure that the candidates are clustered
into a small number of pure clusters, i.e, clusters with either astrophysical
or noise candidates. We then use this performance metric to compare eight
different clustering algorithms. We show that using sky location along with
DM/time improves clustering performance by $\sim$10% as compared to the
traditional DM/time-based clustering. Therefore, positional information should
be used during clustering if it can be made available. We conduct several tests
to compare the performance and generalisability of clustering algorithms to
other transient datasets and propose a strategy that can be used to choose an
algorithm. Our performance metric and clustering strategy can be easily
extended to different single-pulse search pipelines and other astronomy and
non-astronomy-based applications.
