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Suppose we have an undirected graph and we want to efficiently make queries regarding the connected components of that graph, such as:
If the graph is static (not changing), we can simply use breadth-first search to associate a component with each vertex. However, if we want to keep track of these components while adding additional vertices and edges to the graph, a disjoint-set data structure is much more efficient.
We assume the graph is empty initially. Each time we add a vertex, we use MakeSet to make a set containing only that vertex. Each time we add an edge, we use Union to union the sets of the two vertices incident to that edge. Now, each set will contain the vertices of a single connected component, and we can use Find to determine which connected component a particular vertex is in, or whether two vertices are in the same connected component.
This technique is used by the Boost Graph Library to implement its Incremental Connected Components functionality.
Note that this scheme doesn't allow deletion of edges — even without path compression or the rank heuristic, this is not as easy, although more complex schemes have been designed that can deal with this type of incremental update.
When computing the contours of a 3D surface, one of the first steps is to compute the "shorelines," which surround local minima or "lake bottoms." We imagine we are sweeping a plane, which we refer to as the "water level," from below the surface upwards. We will form a series of contour lines as we move upwards, categorized by which local minima they contain. In the end, we will have a single contour containing all local minima.
Whenever the water level rises just above a new local minimum, it creates a small "lake," a new contour line that surrounds the local minimum; this is done with the MakeSet operation.
As the water level continues to rise, it may touch a saddle point, or "pass." When we reach such a pass, we follow the steepest downhill route from it on each side until we arrive a local minimum. We use Find to determine which contours surround these two local minima, then use Union to combine them. Eventually, all contours will be combined into one, and we are done.
In computational chemistry, collisions involving the fragmentation of large molecules can be simulated using molecular dynamics. The result is a list of atoms and their positions. In the analysis, the union-find algorithm can be used to classify these atoms into fragments. Each atom is initially considered to be part of its own fragment. The Find step usually consists of testing the distance between pairs of atoms, though other criterion like the electronic charge between the atoms could be used. The Union merges two fragments together. In the end, the sizes and characteristics of each fragment can be analyzed.
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