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Optimizing search strategies in k-d trees.
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Abstract: While k-d trees have been widely studied and used, their theoretical advantages are often not realized due to ineffective search strategies and generally poor performance in high dimensional spaces. In this paper 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. Our initial method was developed for improving nearest neighbor (NN) search, but has also proven effective for k-NN search and approximate k-NN classification. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | K-d Tree Search Strategy Ineffective Search Strategy Theoretical Advantage Poor Performance Initial Method Unique Method Optimal Depth-first Branch Effective Search Algorithm Path Ordering Approximate K-nn Classification High Dimensional Space K-nn Search |
| Content Type | Text |
| Resource Type | Article |
