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
Multidimensional binary search trees used for associative 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
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Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Introduction to Chemoinformatics
An Introduction to Chemoinformatics
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Previous methods for accelerating Tanimoto queries have been based on using bit strings for representing molecules. No work has gone into examining accelerating Tanimoto queries on real valued descriptors, even though these offer a much more fine grained measure of similarity between molecules. This study utilises a recently discovered reduction from Tanimoto queries to distance queries in Euclidean space to accelerate Tanimoto queries using standard metric data structures. The presented experiments show that it is possible to gain a significant speedup and that general metric data structures are better suited than a data structure tailored for Euclidean space on vectors generated from molecular data.