Pattern Recognition Letters
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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
The String-to-String Correction Problem
Journal of the ACM (JACM)
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
Data Structures and Efficient Algorithms, Final Report on the DFG Special Joint Initiative
Data mining tasks and methods: Classification: nearest-neighbor approaches
Handbook of data mining and knowledge discovery
Searching in Metric Spaces by Spatial Approximation
SPIRE '99 Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware
High Performance Data Mining Using the Nearest Neighbor Join
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Some approaches to improve tree-based nearest neighbour search algorithms
Pattern Recognition
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
A Tabular Pruning Rule in Tree-Based Fast Nearest Neighbor Search Algorithms
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
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A common activity in many pattern recognition tasks, image processing or clustering techniques involves searching a labeled data set looking for the nearest point to a given unlabelled sample. To reduce the computational overhead when the naive exhaustive search is applied, some fast nearest neighbor search (NNS) algorithms have appeared in the last years. Depending on the structure used to store the training set (usually a tree), different strategies to speed up the search have been defined. In this paper, a new algorithm based on the combination of different pruning rules is proposed. An experimental evaluation and comparison of its behavior with respect to other techniques has been performed, using both real and artificial data.