Abstract
Nearest neighbour search is a widely used technique in pattern recognition. During the last three decades a large number of fast algorithms have been proposed. In this work we are interested in algorithms that can be used with any dissimilarity function provided that it fits the mathematical notion of distance. Some of such algorithms organize, in preprocessing time, the data in a tree structure that is traversed in search time to find the nearest neighbour. The speedup is obtained using some pruning rules that avoid the traversal of some parts of the tree. In this work two new decomposition methods to build the tree and three new pruning rules are explored. The behaviour of our proposal is studied through experiments with synthetic and real data.
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