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A k-Closest-Pair Query Algorithm Based on Grid Partition of Hilbert Curve

机译:基于Hilbert曲线网格分区的K-CloseSt-Fire查询算法

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The k-closest-pair query is one of the important operations in spatial database. When the present k-closest-pair algorithms apply to high-dimensional space, their efficiencies are very low. Utilizing the clustering quality of Hilbert curve, divide high-dimensional space into equal-size grids according to the construction of Hilbert curve, and map the points to linear space. The paper presents the definitions and the theory about the method of the dimensionality reduction, gives an algorithm to query k closest pairs based on Hilbert curve. It can delete numerous useless points from data set, so the number of points decreases dramatically. This optimizes scanning procedure, and reduces the running time. The paper proves the correctness of the algorithm. According to the experimental results, the algorithm is better than the method of the brute-force algorithm.
机译:K-CollosEst-Fire查询是Spatial数据库中的重要操作之一。当当前k最近对算法适用于高维空间时,它们的效率非常低。利用Hilbert曲线的聚类质量,根据希尔伯特曲线的构造将高维空间分成相等尺寸的网格,并将点映射到线性空间。本文提出了关于减少维度的方法的定义和理论,给出了一种基于希尔伯特曲线查询k最接近的对的算法。它可以从数据集中删除许多无用的点,因此点数急剧下降。这优化了扫描过程,并减少了运行时间。本文证明了算法的正确性。根据实验结果,该算法优于布鲁斯力算法的方法。

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