Minimum spanning tree (MST)-based clustering is one of the most important clustering techniques in the field of data mining. Although traditional MST-based clustering algorithm has been researched for decades, it still has some limitations for data sets with different density distribution. After analyzing the advantages and disadvantages of the traditional MST-based clustering algorithm, this paper presents two new methods to improve the traditional clustering algorithm. There are two steps of our first method: compute a scaled-MST with scaled distance to find the longest edges between different density clusters and clustering based on the MST. To improve the performance, our second scaled-MST-clustering works by merging the MST construction and inconsistent edges’ detection into one step. To verify the effectiveness and practicability of the proposed method, we apply our algorithm on image segmentation and integration. The encouraging performance demonstrates the superiority of the proposed method on both small data sets and high dimensional data sets.

中文翻译：

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一种基于尺度MST的聚类算法及其在图像分割中的应用

基于最小生成树（MST）的聚类是数据挖掘领域最重要的聚类技术之一。尽管传统的基于 MST 的聚类算法已经研究了几十年，但它对于不同密度分布的数据集仍然存在一些局限性。本文在分析了传统的基于MST的聚类算法的优缺点后，提出了两种新的改进传统聚类算法的方法。我们的第一种方法有两个步骤：计算具有缩放距离的缩放 MST 以找到不同密度集群之间的最长边和基于 MST 的聚类。为了提高性能，我们的第二个缩放 MST 聚类通过将 MST 构造和不一致边缘检测合并为一个步骤来工作。为了验证所提出方法的有效性和实用性，我们将我们的算法应用于图像分割和集成。令人鼓舞的表现证明了所提出的方法在小数据集和高维数据集上的优越性。