SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A fast branch & bound nearest neighbour classifier in metric spaces
Pattern Recognition Letters
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)
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
The choice of reference points in best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
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
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Some approaches to improve tree-based nearest neighbour search algorithms
Pattern Recognition
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
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Many fast similarity search techniques relies on the use of pivots (specially selected points in the data set). Using these points, specific structures (indexes) are built speeding up the search when queering. Usually, pivot selection techniques are incremental, being the first one randomly chosen. This article explores several techniques to choose the first pivot in a tree-based fast similarity search technique. We provide experimental results showing that an adequate choice of this pivot leads to significant reductions in distance computations and time complexity. Moreover, most pivot tree-based indexes emphasizes in building balanced trees.We provide experimentally and theoretical support that very unbalanced trees can be a better choice than balanced ones.