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An Optimization Algorithm for Spatial Index Structure Based on Spatial Clustering

机译:基于空间聚类的空间索引结构优化算法

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Spatial index has been one of the active focus areas in recent database research. The R-tree proposed by Guttman is probably the most popular dynamic index structure for efficiently retrieving objects from a spatial database according to their spatial locations. This paper proposes a new method of constructing R-tree by studying every kind of its operations thoroughly and combining with improved k-medoids clustering algorithm. Because of its more compact structure, the R-tree based on this method has more advantages compared with traditional Rtree. The results of the study show that, due to the optimization of structure, the proposed method can improve index efficiency effectively.
机译:空间索引已成为最近数据库研究中的重点关注领域之一。 Guttman提出的R树可能是最流行的动态索引结构,用于根据对象的空间位置从空间数据库中有效地检索它们。通过深入研究R-tree的各种操作,并结合改进的k-medoids聚类算法,提出一种构造R-tree的新方法。由于其结构更紧凑,因此与传统的Rtree相比,基于该方法的R树具有更多的优势。研究结果表明,由于结构的优化,该方法可以有效地提高索引效率。

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