The minimum spanning tree clustering algorithm is known to be capable of detecting clusters with irregular boundaries. The paper presents a novel hierarchical clustering algorithm based on minimum spanning tree (MST), which tends to reduce the complexity of the merging process with guaranteed clustering performance. There are two core ideas in the proposed method: (1) The inter-cluster distance is calculated with the centroid of MST instead of the center of cluster. (2) The length of cut edge at the intersection of two adjacent clusters is taken as a merge condition. Based on this idea, we propose a three-stage MST-based hierarchical clustering algorithm (CTCEHC). In Stage 1, a preliminary partition is performed with the degrees of vertices in MST. In Stage 2, small subclusters are merged via the geodesic distance between the centroids of MST in two clusters and the cut edge constraint I. In Stage 3, the adjacent cluster pairs satisfying the cut edge constraint II are merged. The experimental results on the synthetic data sets and real data sets demonstrate a good performance of the proposed clustering method.

已知最小生成树聚类算法能够检测具有不规则边界的聚类。提出了一种基于最小生成树（MST）的分层聚类算法，该算法可以降低合并过程的复杂度，并保证聚类性能。所提出的方法有两个核心思想：（1）使用MST的质心而不是簇中心来计算簇间距离。（2）将两个相邻簇的相交处的切边长度作为合并条件。基于此思想，我们提出了一种基于MST的三阶段分层聚类算法（CTCEHC）。在阶段1中，使用MST中的顶点度执行初步划分。在第二阶段 小子类通过两个簇中MST的质心之间的测地距离与切割边缘约束I合并。在阶段3中，合并满足切割边缘约束II的相邻簇对。在综合数据集和真实数据集上的实验结果证明了所提出的聚类方法的良好性能。