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Complexity Analysis for Partitioning Nearest Neighbor Searching Algorithms
June 1996 (vol. 18 no. 6)
pp. 663-668

Abstract—In this paper we present cost estimates for finding the k-nearest neighbors to a test pattern according to a Minkowski p-metric, as a function of the size of the buckets in partitioning searching algorithms. The asymptotic expected number of operations to find the nearest neighbor is presented as a function of the average number of patterns per bucket n and is shown to contain a global minimum.

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Index Terms:
k nearest-neighbor search, nearest-neighbor search, complexity analysis, cost analysis, Minkowski p-metric, k-d tree partitioning, ordered partitioning, product partitioning.
Citation:
Pierre Zakarauskas, John M. Ozard, "Complexity Analysis for Partitioning Nearest Neighbor Searching Algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 6, pp. 663-668, June 1996, doi:10.1109/34.506419
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