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:在统计文献中,对欧几里得观测进行分类的基于密度的集群方法已得到广泛考虑,然而,尚未考虑这一方法用于方向数据;在这项工作中,通过解决与高维空间有关的计算问题,为单位超视距充分确定了基于方向密度的集群方法;我们还提供了一个循环和球体探索工具,用于在考虑内核密度估计方法时研究光滑参数的影响;一项广泛的模拟研究显示了由此得出的圆圈和球体分类程序的绩效;该方法还用于分析外行星数据集。