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It is not absolutely necessary to use a minimum spanning tree.
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To generate the itinerary, we use Algorithm 2. We plan itineraries for mobile agents among CHs by using a minimum spanning tree (MST).
Fourth, the savings algorithm ((mathsf {SV})) takes a different approach, and does not build a circuit using a minimum spanning tree.
Then, it uses a minimum spanning tree algorithm to find the realizations of linear transforms with differences that have the minimum cost and replaces the linear transforms with the required differences.
In our proposed approach, we use a clustering method to group SNs in clusters and select a CH for each cluster for data aggregation, then plan itineraries only among CHs using a minimum spanning tree.
In our proposed approach, we construct clusters based on SN density by using the impact factor, and select the nodes with the highest impact factor as CH, then plan the itinerary among CHs using a minimum spanning tree.
The results were visualised using a minimum spanning tree (MST).
In order to define the relationships among isolates at the microevolutionary level, we performed allelic profile – based comparisons using a minimum spanning tree (MStree) analysis with the BioNumerics v5.10 software (Applied-Maths, Sint Maartens-Latem, Belgium).
We analyzed results using a minimum spanning tree model (8 ).
The relationships between genotypes were represented using a minimum spanning network (MSN).
Clustering analysis of the MLVA26 types using a minimum spanning tree (MST) algorithm grouped the 1,672 isolates into 3 large clusters (A, B, and C), 1 small cluster (D), and 1 singleton (E).
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