论文标题
基于深度的定向数据的聚类分析
Depth-based clustering analysis of directional data
论文作者
论文摘要
提出了针对定向数据的新的基于深度的聚类程序。这种方法是完全非参数，即使在采用合适的深度概念时，也具有灵活性和适用的优点。引入的技术通过广泛的模拟研究进行评估。此外，与其他现有方向聚类算法相比，给出了文本挖掘中的真实数据示例，以解释其有效性。
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.
