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Abstract: Given user data, one often wants to find approximate matches in a large database. A good example of such a task is finding images similar to a given image in a large collection of images. We focus on the important and technically difficult case where each data element is high dimensional, or more generally, is represented by a point in a large metric spaceand distance calculations are computationally expensive. In this paper we introduce a data structure to solve this problem called a GNAT --... (Update)
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BibTeX entry: (Update)
S. Brin. Near neighbor search in large metric spaces. In Proc. VLDB'95, pages 574--584, 1995. http://citeseer.ist.psu.edu/brin95near.html More
@inproceedings{ brin95near,
author = "Sergey Brin",
title = "Near Neighbor Search in Large Metric Spaces",
booktitle = "The {VLDB} Journal",
pages = "574--584",
year = "1995",
url = "citeseer.ist.psu.edu/brin95near.html" }
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85 Data structures and algorithms for nearest neighbor search i.. - Yianilos - 1993
64 A branch and bound algorithm for computing k-nearest neighbo.. (context) - Fukunaga, Narendra - 1975
55 Some approaches to best-match file searching (context) - Burkhard, Keller - 1973
52 Approximate string matching with q-grams and maximal matches (context) - Ukkonen - 1992
35 New techniques for best-match retrieval (context) - Shasha, Wang - 1990
29 Satisfying general proximity / similarity queries with metri.. (context) - Uhlmann - 1991
14 The nearest neighbor problem in an abstract metric space (context) - Feustel, Shapiro - 1982
8 A spatial index for approximate multiple string matching (context) - Bugnion, Fei et al. - 1993
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