GLTree Pro An algorithm for Nearest Neighbour Search.
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GLTree Pro Tags

Finder nearest color nearest Prim Algorithm Sierpinski algorithm search algorithm geostatistics algorithm BWT algorithm algorithm visualization view algorithm Nearest Neighbor Problem find nearest neighbors NSTL Algorithm nearest neighbour find nearest neighbour radius search neighbour Nearest Neighbour Search Nearest Neighbour Filter K-Nearest cuboid Dilation Algorithm build algorithm display algorithm Vigenere Algorithm

GLTree Pro Description

GLTree Pro will provide you with an effective and light-weight algorithm for Nearest Neighbour Search. It has been designed for uniformly random data, but works fine even on sparse ones. If points are too sparse, for example logspace data, search is still performed correctly but speed can degenerate to a brute search algorithm. If query points are close to reference it is very efficient on sparse dataset too. The tree can be build without running any search. The pointer passed to workspace can be used for the above routines. The tree costruction has linear time complexity and it is very fast, so it becomes advantageus against brute search even for a small number of points. In GL-Tree searching has linear complexity (on uniform dataset). Main features: 2D: K-Nearest Neighbor Search K-Nearest Neighbour Graph Nearest Neighbour Filter Radius Search Cuboid Search BuildTree DeleteTree 3D: K-Nearest Neighbor Search K-Nearest Neigbour Graph Nearest Neighbour Filter Radius Search Cuboid Search BuildTree DeleteTree

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