Kinetic Clustering of Points on the Line
classification
💻 cs.CG
cs.DS
keywords
pointsclusteringlineminimizingalgorithmsapproximationassumptionsclassical
read the original abstract
The problem of clustering a set of points moving on the line consists of the following: given positive integers n and k, the initial position and the velocity of n points, find an optimal k-clustering of the points. We consider two classical quality measures for the clustering: minimizing the sum of the clusters diameters and minimizing the maximum diameter of a cluster. For the former, we present polynomial-time algorithms under some assumptions and, for the latter, a (2.71 + epsilon)-approximation.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.