Abstract
AbstractSpanning trees for large dataset graphs having millions of data points as nodes and almost \( n^2\) edges for a dataset of n points are challenging tasks. Many recent advancements have been made in the design of a spanning tree with millions of points. This is survey work on some recent developments in spanning-tree specific to large data sets. Generally, the efficiency of any design is examined by its time and space complexity. Several partitioning algorithms, clustering models, edge selection techniques, and merging methodologies are examined and evaluated in this paper, along with their respective efficiency. The review spotted a common paradigm, and some strategies, such as Delaunay Triangulation, Hilbert Curve and others, may have research potential for huge datasets.KeywordsMinimum spanning treesLarge datasetsPartitioningDelaunay triangulationEfficiency
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