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On Minimum Sum of Radii and Diameters Clustering

机译:半径和直径聚类的最小和

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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:给定一个度量(V,d)和一个整数k,我们考虑用最多k个簇覆盖V点的问题,以使半径之和或这些簇的直径之和最小。前一个问题称为最小和半径(MSR)问题,后一个问题称为最小和直径(MSD)问题。当前针对这些问题的最佳多项式时间算法的逼近率分别为3.504和7.008 [2]。对于MSR问题,当度量标准是未加权图的最短路径度量标准且不能有任何单例群集时,我们给出一种精确的算法。对于具有欧式距离的平面上的MSD问题,我们提出了多项式时间逼近方案。

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