最小生成树(SMT),Steiner Minimum Tree(SMT),音标,读音,翻

1) Steiner Minimum Tree(SMT)

2) minimum spanning tree

1.
Research on bi-criteria minimum spanning tree problem based on ant colony system;

2.
The solution-based DPCNN to the minimum spanning tree of undirected weighted graph;

3.
Degree-constrained minimum spanning tree algorithm based on immune-ant colony algorithm;

3) minimal spanning tree

1.
A color image segmentation method based on Minimal Spanning Tree and local thresholds;

2.
Application of Minimal Spanning Tree in Supplies of Central Heating;

3.
Prim algorithm of minimal spanning tree and minimum function;

4) minimum cost spanning tree

1.
On Algorithm of Producing Minimum Cost Spanning Tree by Method of Seeking Cycles to Romove Its Edge;

2.
The Application of Minimum Cost Spanning Tree to Solve the Question of Urban Highway

3.
The sparse feature difference degree and the minimum cost spanning tree are used to resolve the problem of high attribute dimensional data clustering which exists in information classifying,in order to support the central idea,an example is given in the paper.

5) MST

1.
MST problems solved by DNA-genetic algorithm;

2.
MST Clustering Algorithm Based on Grid;

3.
A branch and bound algorithm for the CMST problem;

6) Minimum Spanning Tree(MST)

1.
To resolve the NP-complete problem of Minimum Spanning Tree(MST) calculations and meet the performance requirements in actualnetwork environment, a distributed algorithm for searching the MST is proposed.

2.
The minimum spanning tree(MST) was used to obtain the best connected-component of the image set to recover the transformation between images and project the images into the mosaic frame.

3.
Pearson\'s correlation coefficient was used to compute the correlation matrix of price fluctuations,and then this matrix was taken as the adjacent matrix of a weighted-complete-graph,to construct the correlation network/tree of stocks using an improved Minimum Spanning Tree(MST) algorithm.

• 它包含图中的每个顶点。
• 它的所有边上的权的总和尽可能小。

$w\left(T\right)=\sum_\left\{\left(u,v\right)\in T\right\} w\left(u,v\right)$