  We review and expand on a Bayesian model selection technique for the
detection of gravitational waves from neutron star ring-downs associated with
pulsar glitches. The algorithm works with power spectral densities constructed
from overlapping time segments of gravitational wave data. Consequently, the
original approach was at risk of falsely identifying multiple signals where
only one signal was present in the data. We introduce an extension to the
algorithm which uses posterior information on the frequency content of detected
signals to cluster events together. The requirement that we have just one
detection per signal is now met with the additional bonus that the belief in
the presence of a signal is boosted by incorporating information from adjacent
time segments.
