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In this paper we show the results of a performance comparison between two Nearest Neighbour Search Methods: one, proposed by Arya & Mount, is based on a kd–tree data structure and a Branch and Bound approximate search algorithm [1], and the other is a search method based on dimensionality projections, presented by Nene & Nayar in [5]. A number of experiments have been carried out in order to find the best choice to work with high dimensional points and large data sets.

Comparison of Two Fast Nearest-Neighbour Search Methods in High-Dimensional Large-Sized Databases