applied to datasets which are composed of clusters with diverse shapes, sizes, and
densities. To alleviate these deficiencies, we propose a novel split-and-merge hierarchical
clustering method in which a minimum spanning tree (MST) and an MST-based graph are
employed to guide the splitting and merging process. In the splitting process, vertices with
high degrees in the MST-based graph are selected as initial prototypes, and K-means is …