Proximity Cluster Trees

Elena Jakubiak Hutchinson, Sarah F. Frisken, R. N. Perry
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引用次数: 3

Abstract

Hierarchical spatial data structures provide a means for organizing data for efficient processing. Most spatial data structures are optimized for performing queries, such as intersection and containment testing, on large data sets. Set-up time and complexity of these structures can limit their value for small data sets, an often overlooked yet important category in geometric processing. We present a new hierarchical spatial data structure, dubbed a proximity cluster tree, which is particularly effective on small data sets. Proximity cluster trees are simple to implement, require minimal construction overhead, and are structured for fast distance-based queries. Proximity cluster trees were tested on randomly generated sets of 2D Bézier curves and on a text-rendering application requiring minimum-distance queries to 2D glyph outlines. Although proximity cluster trees were tailored for small data sets, empirical tests show that they also perform well on large data sets.
邻近聚类树
分层空间数据结构提供了一种有效处理数据的组织方法。大多数空间数据结构都针对在大型数据集上执行查询进行了优化,例如交集和包含测试。这些结构的设置时间和复杂性可能会限制它们对小数据集的价值,小数据集是几何处理中经常被忽视但很重要的一类。我们提出了一种新的分层空间数据结构,称为邻近聚类树,它对小数据集特别有效。邻近簇树易于实现,需要最小的构造开销,并且用于快速的基于距离的查询。在随机生成的2D bsamzier曲线集和需要对2D字形轮廓进行最小距离查询的文本渲染应用程序上测试了邻近簇树。虽然邻近聚类树是为小数据集量身定制的,但经验测试表明,它们在大数据集上也表现良好。
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