  A new depth-based clustering procedure for directional data is proposed. Such
method is fully non-parametric and has the advantages to be flexible and
applicable even in high dimensions when a suitable notion of depth is adopted.
The introduced technique is evaluated through an extensive simulation study. In
addition, a real data example in text mining is given to explain its
effectiveness in comparison with other existing directional clustering
algorithms.
