An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Vorono trees and clustering problems
Information Systems
ACM Computing Surveys (CSUR)
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
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
A GPU-Based Implementation for Range Queries on Spaghettis Data Structure
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
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We introduce a novel metric space search data structure called EGNAT, which is fully dynamic and designed for secondary memory. The EGNAT is based on Brin's GNAT static index, and partitions the space according to hyperplanes. The EGNAT implements deletions using a novel technique dubbed Ghost Hyperplanes, which is of independent interest for other metric space indexes. We show experimentally that the EGNAT is competitive with the M-tree, the baseline for this scenario.