Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
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
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Incremental Construction of Neighborhood Graphs for Nonlinear Dimensionality Reduction
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Incremental Neighborhood Graphs Construction for Multidimensional Databases Indexing
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Efficient computation of elliptic gabriel graph
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
An effective method for locally neighborhood graphs updating
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
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Indexing is the most effective technique to speed up queries in databases. While traditional indexing approaches are used for exact search, a query object may not be always identical to an existing data object in similarity search. This paper proposes a new dynamic data structure called Hypherspherical Region Graph (HRG) to efficiently index a large volume of data objects as a graph for similarity search in metric spaces. HRG encodes the given dataset in a smaller number of vertices than the known graph index, Incremental-RNG, while providing flexible traversal without incurring backtracking as observed in tree-based indices. An empirical analysis performed on search time shows that HRG outperforms Incremental-RNG in both cases. HRG, however, outperforms tree-based indices in range search only when the data dimensionality is not so high.