What are the significant applications of minimum spanning trees?
After doing some research online and in several textbooks, I have found three real-world applications:
Building a connected network. There are scenarios where we have a limited set of possible routes, and we want to select a subset that will make our network (e.g., electrical grid, computer network) fully connected at the lowest cost.
Clustering. If you want to cluster a bunch of points into $k$ clusters, then one approach is to compute a minimum spanning tree and then drop the $k-1$ most expensive edges of the MST. This separates the MST into a forest with $k$ connected components; each component is a cluster. (I confess I'm not very clear on whether anyone uses this clustering method in practice, and if so, what domains it is useful in.)
Traveling salesman problem. There's a straightforward way to use the MST to get a $2$-approximation to the optimal solution to the traveling salesman problem, and the Christofides' heuristic uses MSTs to get a $1.5$-approximation. (One could reasonably question how real-world this is, though, as there are other approximation algorithms for the traveling salesman problem that will typically do even better in practice.)
I have found vague references to other applications, but I have not found details. Are there other significant real-world applications of minimum spanning trees, and if so, how do they work?