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XM-tree: data driven computational model by using metric extended nodes with non-overlapping in high-dimensional metric spaces

机译:XM-Tree:数据驱动的计算模型使用度量扩展节点,在高维度量空间中具有非重叠

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Finding similar objects based on a query and a distance, remains a fundamental problem for many applications. The general problem of many similarity measures is to focus the search on as few elements as possible to find the answer. The index structures divides the target dataset into subsets. With large amounts of data, the volumes of the subspaces grow exponentially, that will affect the search algorithms. This problem is caused by inherent deficiencies of space partitioning, and also, the overlap factor between regions. This methods have proven to be unreliable, it becomes hard to store, manage, and analyze these quantities. The research tends to degenerate into a complete analysis of the data set. In this paper, we propose a new indexing technique called XM-tree, that partitions the space using spheres. The idea is to combine two structures, arborescent and sequential, in order to limit the volume of the outer regions of the spheres, by creating extended regions and inserting them into linked lists named extended regions, and also by excluding of the empty setsseparable partitionsthat do not contain objects. The goal is to eliminate some objects without the need to compute their relative distances to a query object. Therefore, we proposed a parallel version of the structure on a set of real machine. We also discuss the efficiency of the construction and querying phases, and the quality of our index by comparing it with recent techniques.
机译:基于查询和距离查找类似的对象,仍然是许多应用程序的根本问题。许多相似性措施的一般问题是将搜索集中在尽可能少的元素以找到答案。索引结构将目标数据集划分为子集。具有大量数据,子空间的卷以指数为指数,这将影响搜索算法。此问题是由空间分区的固有缺陷引起的,并且区域之间的重叠因子引起。这种方法已被证明是不可靠的,它变得难以存储,管理和分析这些数量。该研究倾向于对数据集的完全分析。在本文中,我们提出了一种新的索引技术,称为XM树,将空间使用球形分区。这个想法是通过创建扩展区域并将它们插入名为扩展区域的链接列表来限制球体的外部区域的体积,以限制球体的外部区域的体积,并且还通过排除空的setsepable partitionsthat。不包含对象。目标是消除某些对象,而无需将其相对距离计算到查询对象。因此,我们提出了一组真实机器上的结构的并行版本。我们还通过将其与最近的技术进行比较来讨论建筑和查询阶段的效率,以及我们指数的质量。

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