Pattern Recognition Letters
A fast branch & bound nearest neighbour classifier in metric spaces
Pattern Recognition Letters
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Some approaches to best-match file searching
Communications of the ACM
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Some approaches to improve tree-based nearest neighbour search algorithms
Pattern Recognition
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
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In this paper, we present a novel class of prune rules for metric indexing algorithms. The rules are derived from the geometrical properties in the low dimensional embedding space and are applicable to positive semi-definite metrics. The proposed prune rules are cheap both in computation and storage cost and can be readily incorporated with available metric indexing structures. In the simulation experiments, the Geometric Near-neighbor Access Tree with the proposed prune rules shows preferable pruning ability especially on large datasets with high dimensions.