One other way to check the performance of clustering algorithms is to look at the clustering in the parameter space composed of parameters that were not used in the clustering processes. Interestingly, DBScan (more) successfully identifies at least one distinct population (around width ~ 0.5; green in the DBScan plot) in the width-height space, which can be recognized by human eyes. There are less points in the DBScan plot, because the "noisy samples" (data points that cannot be clustered; black, smaller points in the movies) are removed.