Optimizing Search Strategies in k-d Trees

K-d trees have been widely studied and used, yet their theoretical advantages are often not realized due to ineffective search strategies and degrading performance in high dimensional spaces. We outline an effective search algorithm for k-d trees that combines an optimal depth-first branch and bound (DFBB) strategy with a unique method for path ordering and pruning. This technique was developed for improving nearest neighbor (NN) search, but has also proven effective for k-NN search and approximate k-NN queries.