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
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A Probabilistic Spell for the Curse of Dimensionality
ALENEX '01 Revised Papers from the Third International Workshop on Algorithm Engineering and Experimentation
Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances
The VLDB Journal — The International Journal on Very Large Data Bases
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
Clustering-based similarity search in metric spaces with sparse spatial centers
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
An index data structure for searching in metric space databases
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
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In this paper, we study the well-known algorithm of Bentley and Saxe in the context of similarity search in metric spaces. We apply the algorithm to existing static metric index structures, obtaining dynamic ones. We show that the overhead of the Bentley-Saxe method is quite low, and because it facilitates the dynamic use of any state-of-the-art static index method, we can achieve results comparable to, or even surpassing, existing dynamic methods. Another important contribution of our approach is that it is very simple--an important practical consideration. Rather than dealing with the complexities of dynamic tree structures, for example, the core index can be built statically, with full knowledge of its data set.