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Nearest neighbor queries
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Source International Conference on Management of Data archive
Proceedings of the 1995 ACM SIGMOD international conference on Management of data table of contents
San Jose, California, United States
Pages: 71 - 79  
Year of Publication: 1995
ISBN:0-89791-731-6
Also published in ...
Authors
Nick Roussopoulos  Department of Computer Science, University of Maryland, College Park, MD
Stephen Kelley  Department of Computer Science, University of Maryland, College Park, MD
Frédéric Vincent  Department of Computer Science, University of Maryland, College Park, MD
Sponsors
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 63,   Downloads (12 Months): 413,   Citation Count: 246
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ABSTRACT

A frequently encountered type of query in Geographic Information Systems is to find the k nearest neighbor objects to a given point in space. Processing such queries requires substantially different search algorithms than those for location or range queries. In this paper we present an efficient branch-and-bound R-tree traversal algorithm to find the nearest neighbor object to a point, and then generalize it to finding the k nearest neighbors. We also discuss metrics for an optimistic and a pessimistic search ordering strategy as well as for pruning. Finally, we present the results of several experiments obtained using the implementation of our algorithm and examine the behavior of the metrics and the scalability of the algorithm.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Sproull, R.F., "Refinements to Nearest- Neighbor Searching in k-Dimensional Trees," A1- gorithmica, 6, 1987, pp. 579-589.

CITED BY  246

Collaborative Colleagues:
Nick Roussopoulos: colleagues
Stephen Kelley: colleagues
Frédéric Vincent: colleagues