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.

翻译：提出了新的定向数据深度分组程序,这种方法完全不具有参数性,在采用适当的深度概念时即使在高层次上也具有灵活性和可适用性,采用的技术通过广泛的模拟研究加以评价,此外,在文字挖掘中树立了一个真正的数据实例,以解释与其他现有的定向分组算法相比,其有效性。