Web Reference: One of the cleanest ways to cut down a search space when working out point proximity! Mike Pound explains K-Dimension Trees. ...more In computer science, a k-d tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k -dimensional space. K-dimensional is that which concerns exactly k orthogonal axes or a space of any number of dimensions. I've played with both kd- and quad [and oct] trees. kd's are (usually) more efficient if the nearest neighbor search is against a fixed data set. The reason why is that kd trees are expensive to rebalance. (If you're constantly adding new points to the data set this will be a problem.)
YouTube Excerpt: One of the cleanest ways to cut down a search space when working out point proximity! Mike Pound explains K-Dimension
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