We introduce a new method for clustering based on Cluster Catch Digraphs (CCDs). The new method addresses the limitations of RK-CCDs by employing a new variant of spatial randomness test that employs the nearest neighbor distance (NND) instead of the Ripley's K function used by RK-CCDs. We conduct a comprehensive Monte Carlo analysis to assess the performance of our method, considering factors such as dimensionality, data set size, number of clusters, cluster volumes, and inter-cluster distance. Our method is particularly effective for high-dimensional data sets, comparable to or outperforming KS-CCDs and RK-CCDs that rely on a KS-type statistic or the Ripley's K function. We also evaluate our methods using real and complex data sets, comparing them to well-known clustering methods. Again, our methods exhibit competitive performance, producing high-quality clusters with desirable properties. Keywords: Graph-based clustering, Cluster catch digraphs, High-dimensional data, The nearest neighbor distance, Spatial randomness test

翻译：本文提出了一种基于聚类捕获有向图（CCDs）的新聚类方法。该方法通过采用一种新型空间随机性检验来解决RK-CCDs的局限性，该检验使用最近邻距离（NND）替代了RK-CCDs中使用的Ripley's K函数。我们进行了全面的蒙特卡洛分析以评估该方法的性能，考虑了维度、数据集大小、聚类数量、聚类体积和聚类间距离等因素。该方法尤其适用于高维数据集，其性能与依赖KS型统计量或Ripley's K函数的KS-CCDs和RK-CCDs相当或更优。我们还使用真实和复杂数据集评估了该方法，并与知名聚类方法进行了比较。结果表明，该方法展现出具有竞争力的性能，能够生成具有理想特性的高质量聚类。关键词：基于图的聚类，聚类捕获有向图，高维数据，最近邻距离，空间随机性检验