2024/06/03 by Richard Lettich, Lettich, Richard
Computer Science · #Data Mining Algorithms and Applications
paper · pdf · doi:10.48550/arxiv.2406.01739
We introduce a decomposition method for the distributed calculation of exact Euclidean Minimum Spanning Trees in high dimensions (where sub-quadratic algorithms are not effective), or more generalized geometric-minimum spanning trees of complete graphs, where for each vertex v∈ V in the graph G=(V,E) is represented by a vector in v∈ ℝn, and each for any edge, the the weight of the edge in the graph is given by a symmetric binary `distance' function between the representative vectors w(\x,y\) = d(x,y). This is motivated by the task of clustering high dimensional embeddings produced by neural networks, where low-dimensional algorithms are ineffective; such geometric-minimum spanning trees find applications as a subroutine in the construction of single linkage dendrograms, as the two structures can be converted between each other efficiently.