Searching in metric spaces with user-defined and approximate distances
ACM Transactions on Database Systems (TODS)
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
M+-tree: a new dynamical multidimensional index for metric spaces
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Pivot selection techniques for proximity searching in metric spaces
Pattern Recognition Letters
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Dynamic similarity search in multi-metric spaces
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
BM+-Tree: a hyperplane-based index method for high-dimensional metric spaces
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Nearest neighbours search using the PM-Tree
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
New dynamic construction techniques for M-tree
Journal of Discrete Algorithms
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The M-tree and its variants have been proved to provide an efficient similarity search in database environments. In order to further improve their performance, in this paper we propose an extension of the M-tree family, which makes use of nearest-neighbor (NN) graphs. Each tree node maintains its own NN-graph, a structure that stores for each node entry a reference (and distance) to its nearest neighbor, considering just entries of the node. The NN-graph can be used to improve filtering of non-relevant subtrees when searching (or inserting new data). The filtering is based on using "sacrifices" - selected entries in the node serving as pivots to all entries being their reverse nearest neighbors (RNNs). We propose several heuristics for sacrifice selection; modified insertion; range and kNN query algorithms. The experiments have shown the M-tree (and variants) enhanced by NN-graphs can perform significantly faster, while keeping the construction cheap.