  In data mining, density-based clustering, which entails classifying
datapoints according to their distributions in some space, is an essential
method to extract information from large datasets. With the advent of
software-based radio, ionospheric radars are capable of producing
unprecedentedly large datasets of plasma turbulence backscatter observations,
and new automatic techniques are needed to sift through them. We present an
algorithm to automatically identify and track clusters of radar echoes through
time, using dbscan, a celebrated density-based clustering method for noisy
point-clouds. We demonstrate its efficiency by tracking turbulent structures in
the E-region ionosphere, the so-called radar aurora. Through conjugate auroral
imagery, as well as in-situ satellite observations, we demonstrate that the
observed turbulent structures generally track the motion of the aurora. We
discuss instances when this prediction is brought to fruition and when it
ostensibly is not. Through case studies, we highlight the important instances
when the radar echo bulk motions vary considerably around discrete auroral
arcs, an effect we argue is produced by strong electric field modulations
caused by energetic particle precipitation.
