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ABSTRACT
This paper gives an algorithm for approximately solving the post office problem: given n points (called sites) in d dimensions, build a data structure so that, given a query point q, a closest site to q can be found quickly. The algorithm is also given a relative error bound &egr;, and depends on a ratio &rgr;, which is no more than the ratio of the distance between the farthest pair of sites to the distance between the closest pair of sites. The algorithm builds a data structure of size O(n&eegr;)O(1/&egr;)(d−1)/2 in time O(n2&eegr;)O(1/&egr;)(d−1). Here &eegr;=log(&rgr;/&egr;). With this data structure, a site is returned whose distance to a query point q is within 1+&egr; of the distance of the closest site. A query needs O(logn)O(1/&egr;)(d−1)/2 time, with high probability.
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CITED BY 22
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Amit Chakrabarti , Bernard Chazelle , Benjamin Gum , Alexey Lvov, A lower bound on the complexity of approximate nearest-neighbor searching on the Hamming cube, Proceedings of the thirty-first annual ACM symposium on Theory of computing, p.305-311, May 01-04, 1999, Atlanta, Georgia, United States
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Eyal Kushilevitz , Rafail Ostrovsky , Yuval Rabani, Efficient search for approximate nearest neighbor in high dimensional spaces, Proceedings of the thirtieth annual ACM symposium on Theory of computing, p.614-623, May 24-26, 1998, Dallas, Texas, United States
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Allan Borodin , Rafail Ostrovsky , Yuval Rabani, Lower bounds for high dimensional nearest neighbor search and related problems, Proceedings of the thirty-first annual ACM symposium on Theory of computing, p.312-321, May 01-04, 1999, Atlanta, Georgia, United States
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