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On Minimum Sum of Radii and Diameters Clustering

机译:关于最小数量的半径和直径聚类

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Given a metric (V, d) and an integer k, we consider the problem of covering the points of V with at most k clusters so as to minimize the sum of radii or the sum of diameters of these clusters. The former problem is called the Minimum Sum Radii (MSR) problem and the latter is the Minimum Sum Diameters (MSD) problem. The current best polynomial time algorithms for these problems have approximation ratios 3.504 and 7.008, respectively [2]. For the MSR problem, we give an exact algorithm when the metric is the shortest-path metric of an unweighted graph and there cannot be any singleton clusters. For the MSD problem on the plane with Euclidean distances, we present a polynomial time approximation scheme.
机译:给定度量(V,D)和整数k,我们考虑在大多数K簇中覆盖V点的问题,以便最小化半径的总和或这些簇的直径之和。以前的问题被称为最小和最小的半径(MSR)问题,后者是最小总和直径(MSD)问题。这些问题的当前最佳多项式时间算法分别具有近似值3.504和7.008 [2]。对于MSR问题,我们在度量标准是未加权图形的最短路径度量时,我们提供了一个精确的算法,并且不能有任何单例群集。对于具有欧几里德距离的平面上的MSD问题,我们介绍了多项式时间近似方案。

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