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Concept
Kruskal's algorithm
Summary
Kruskal's algorithm (also known as Kruskal's method) finds a minimum spanning forest of an undirected edge-weighted graph. If the graph is connected, it finds a minimum spanning tree. (A minimum spanning tree of a connected graph is a subset of the edges that forms a tree that includes every vertex, where the sum of the weights of all the edges in the tree is minimized. For a disconnected graph, a minimum spanning forest is composed of a minimum spanning tree for each connected component.) It is a greedy algorithm in graph theory as in each step it adds the next lowest-weight edge that will not form a cycle to the minimum spanning forest.
This algorithm first appeared in Proceedings of the American Mathematical Society, pp. 48–50 in 1956, and was written by Joseph Kruskal. It was rediscovered by .
Other algorithms for this problem include Prim's algorithm, the reverse-delete algorithm, and Borůvka's algorithm.
create a forest F (a set of trees), where each vertex in the graph is a separate tree
create a sorted set S containing all the edges in the graph
while S is nonempty and F is not yet spanning
remove an edge with minimum weight from S
if the removed edge connects two different trees then add it to the forest F, combining two trees into a single tree
At the termination of the algorithm, the forest forms a minimum spanning forest of the graph. If the graph is connected, the forest has a single component and forms a minimum spanning tree.
The following code is implemented with a disjoint-set data structure. Here, we represent our forest F as a set of edges, and use the disjoint-set data structure to efficiently determine whether two vertices are part of the same tree.
algorithm Kruskal(G) is
F:= ∅
for each v ∈ G.V do
MAKE-SET(v)
for each (u, v) in G.E ordered by weight(u, v), increasing do
if FIND-SET(u) ≠ FIND-SET(v) then
F:= F ∪ {(u, v)} ∪ {(v, u)}
UNION(FIND-SET(u), FIND-SET(v))
return F
For a graph with E edges and V vertices, Kruskal's algorithm can be shown to run in O(E log E) time, or equivalently, O(E log V) time, all with simple data structures.
Official source
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Ontological neighbourhood