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.
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The authors would like to thank the Chinese National Science Foundation for its valuable support of this work under award 61473220 and all the anonymous reviewers and editors for their valuable comments.
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Li, J., Wang, X. & Wang, X. A scaled-MST-based clustering algorithm and application on image segmentation. J Intell Inf Syst 54, 501–525 (2020). https://doi.org/10.1007/s10844-019-00572-x
- Minimum spanning tree
- Minimum spanning tree-based clustering
- Image segmentation
- Image integration