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An accurate and efficient algorithm for determining minimum circumscribed circles and spheres from discrete data points

机译:从离散数据点确定最小外接圆和球的准确高效算法

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This paper presents a novel combinatorial search algorithm for determining minimum circumscribed (MC) circles and spheres from discrete data points. The presented algorithm is able to efficiently identify the essential subset of the input data points to construct the MC circle/sphere for the entire data set. The common issue of computational explosion for a large data set due to a greatly increased number of combinatorial searches is thus of no concern. The main feature of this work is the derivation of an innovative geometric property, named the Integrated Property (IP), for a unique three-point subset in 2D or a unique four-point subset in 3D. The significance is that the MC circle/sphere for the identified IP point subset is in fact the MC circle/sphere for the entire data set. As the unique IP point subset can always be found, the presented algorithm is guaranteed to yield exact MC circle/sphere solutions. A related data exchange scheme is formulated to efficiently identify the unique IP point subset from the input point set. The expected computational complexity of the search algorithm is quantified to be O(n log n) through a large number of computational test cases.
机译:本文提出了一种新颖的组合搜索算法,用于从离散数据点确定最小外接圆(MC)圆和球。提出的算法能够有效地识别输入数据点的基本子集,以构造整个数据集的MC圆/球。因此,由于大大增加了组合搜索的数量而导致的大数据集计算爆炸的常见问题就不用担心了。这项工作的主要特点是为2D中唯一的三点子集或3D中唯一的四点子集推导了一个创新的几何特性,称为集成特性(IP)。重要性在于,所标识的IP点子集的MC圈/球实际上就是整个数据集的MC圈/球。由于总是可以找到唯一的IP点子集,因此可以保证所提出的算法产生精确的MC圆/球解决方案。制定了相关的数据交换方案,以从输入点集中有效地标识唯一的IP点子集。通过大量的计算测试案例,搜索算法的预期计算复杂度被量化为O(n log n)。

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