An efficient nearest neighbor search in high-dimensional data spaces

被引:0
|
作者
Lee, DH [1 ]
Kim, HJ [1 ]
机构
[1] Seoul Natl Univ, Dept Comp Engn, Gwanak Gu, Seoul 151742, South Korea
关键词
algorithms; incremental nearest neighbor search; high-dimensional index structure; SPY-TEC;
D O I
10.1016/S0020-0190(01)00236-8
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
An incremental nearest neighbor search algorithm for high dimensional data spaces was presented. MinDist, a new metric was introduced for guiding an ordered best-first traversal when finding nearest neighbors on the SPY-TEC, an efficient index structure for similarity search in high-dimensional spaces. The practical impact of the algorithm on the SPY-TEC was calculated and compared with the R tree, X tree and the sequential scan.
引用
收藏
页码:239 / 246
页数:8
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