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In the k-nearest neighborhood model (k-NN), we are given a set of points P, and we shall answer queries q by returning the k nearest neighbors of q in P according to some metric. This concept is crucial in many areas of data analysis and data processing, e.g., computer vision, document retrieval and machine learning. Many k-NN algorithms have been published and implemented, but often the relation between parameters and accuracy of the computed k-NN is not explicit. We study property testing ofarXiv:1810.05064v3 fatcat:wwps6i7qjnhmhgczau4ai5asty