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：基于密度的聚类方法在统计学文献中被广泛用于欧几里得观测数据的分类。然而，该方法尚未应用于方向数据。本文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方向密度聚类方法。我们还提供了圆形和球面探索性工具，用于研究采用核密度估计方法时平滑参数的影响。通过广泛的模拟研究，展示了所提分类流程在圆和球面上的性能。该方法还被应用于分析系外行星数据集。