  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.
