  Density-based clustering methodology has been widely considered in the
statistical literature for classifying Euclidean observations. However, this
approach has not been contemplated for directional data yet. In this work,
directional density-based clustering methodology is fully established for the
unit hypersphere by solving the computational problems associated to high
dimensional spaces. We also provide a circular and spherical exploratory tool
for studying the effect of the smoothing parameter when kernel density
estimation methods are considered. An extensive simulation study shows the
performance of the resulting classification procedure for the circle and for
the sphere. The methodology is also applied to analyse an exoplanets dataset.
