  We develop a new density-based clustering algorithm named CRAD which is based
on a new neighbor searching function with a robust data depth as the
dissimilarity measure. Our experiments prove that the new CRAD is highly
competitive at detecting clusters with varying densities, compared with the
existing algorithms such as DBSCAN, OPTICS and DBCA. Furthermore, a new
effective parameter selection procedure is developed to select the optimal
underlying parameter in the real-world clustering, when the ground truth is
unknown. Lastly, we suggest a new clustering framework that extends CRAD from
spatial data clustering to time series clustering without a-priori knowledge of
the true number of clusters. The performance of CRAD is evaluated through
extensive experimental studies.
