Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST.

翻译：现有的大多数聚类方法基于单一粒度的信息，例如数据的距离和密度。这种基于最细粒度的方法通常效率低下且易受噪声影响。因此，我们提出一种结合多粒度粒球与最小生成树（MST）的聚类算法。该方法通过构建粗粒度粒球，并利用粒球与最小生成树实现基于“大尺度优先”的聚类策略，从而大幅降低离群点的影响，并加速最小生成树的构建过程。在多个数据集上的实验结果验证了该算法的有效性。所有代码已开源至https://github.com/xjnine/GBMST。