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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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摘要

Given a metric and an integer , we consider the problem of partitioning the points of into at most 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 of radii (MSR) problem and the latter is the minimum sum of diameters (MSD) problem. The current best polynomial time algorithms for these problems have approximation ratios 3.504 and 7.008, respectively. We call a cluster containing a single point, a singleton cluster. For the MSR problem when singleton clusters are not allowed, we give an exact algorithm for metrics induced by unweighted graphs. In addition, we show that in this case, a solution consisting of the best single cluster for each connected component of the graph is a -approximation algorithm. For the MSD problem on the plane with Euclidean distances, we present a polynomial time approximation scheme. In addition, we settle the open problem of complexity of the MSD problem with constant by giving a polynomial time exact algorithm in this case. The previously best known approximation algorithms for MSD on the plane or for MSD with constant have both ratio 2.
机译:给定一个度量和一个整数,我们考虑将点划分为最多簇的问题,以使半径之和或这些簇的直径之和最小。前者称为最小半径总和(MSR)问题,后者称为直径最小总和(MSD)问题。针对这些问题的当前最佳多项式时间算法分别具有近似比3.504和7.008。我们称一个包含单点的群集为单例群集。对于不允许单例聚类的MSR问题,我们给出了一种针对未加权图诱导的度量的精确算法。此外,我们表明在这种情况下,由图的每个连接部分的最佳单个群集组成的解决方案是-近似算法。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。另外,在这种情况下,通过给出多项式时间精确算法,我们用常数解决了MSD问题复杂性的开放问题。平面上MSD或常数MSD的先前最著名的近似算法的比率均为2。

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