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.

翻译：密度聚类方法在统计文献中被广泛用于欧几里得观测数据的分类，但该方法尚未应用于方向性数据。本研究通过解决高维空间相关的计算问题，在单位超球面上完整建立了基于密度的方向性聚类方法。我们还提供了一种圆形和球面探索性工具，用于研究在采用核密度估计方法时光滑参数的影响。大量模拟研究展示了所提出的分类方法在圆面和球面上的表现。该方法还被应用于分析一个系外行星数据集。