【摘要】 在低维空间中R树的查询效率较高,而在高维空间中其性能急剧恶化,降维成为解决问题的关键。利用Hilbert曲线的降维特性,该文提出基于Hilbert曲线近似k-最近邻查询算法AKNN,分析近似k-最近邻的误差。实验结果表明算法在执行时间上优于线性扫描和基于R树最短优先查询算法,近似解的质量较好。更多还原

【Abstract】 R-Tree can achieve better performance in low-dimensional space,but its performance suffers greatly in high-dimensional space.So the reduction of the dimensionality is the key to the problem.Hilbert curve can filld dimensional space linearly,divide it into equal-size grids and map points lying in grids into linear space.Using the quality of reducing dimensions of Hilbert curve,the paper presents an approximate k-nearest neighbors query algorithm,and analyzes the quality of the approximate k-nearest neighbors.According to the test,its running time is shorter than brute-force method and the algorithm based on R-tree,and the quality of approximate k-nearest neighbors is better.更多还原