Approximation algorithms for the metric maximum clustering problem with given cluster sizes

Authors:
Refael Hassin;Shlomi Rubinstein
Affiliations:
Department of Statistics and Operations Research, School of Mathematical Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel;Department of Statistics and Operations Research, School of Mathematical Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel
Venue:
Operations Research Letters
Year:
2003

Citing 4
Cited 3

Probabilistic construction of deterministic algorithms: approximating packing integer programs

Journal of Computer and System Sciences - 27th IEEE Conference on Foundations of Computer Science October 27-29, 1986
One-half approximation algorithms for the k-partition problem

Operations Research - Supplement
Robust Matchings

SIAM Journal on Discrete Mathematics
Approximation algorithms for maximum dispersion

Operations Research Letters

An improved approximation algorithm for the metric maximum clustering problem with given cluster sizes

Information Processing Letters
Sphere-separable partitions of multi-parameter elements

Discrete Applied Mathematics
An improved approximation algorithm for the metric maximum clustering problem with given cluster sizes

Information Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

The input to the METRIC MAXIMUM CLUSTERING PROBLEM WITH GIVEN CLUSTER SIZES consists of a complete graph G=(V,E) with edge weights satisfying the triangle inequality, and integers c"1,...,c"p that sum to |V|. The goal is to find a partition of V into disjoint clusters of sizes c"1,...,c"p, that maximizes the sum of weights of edges whose two ends belong to the same cluster. We describe approximation algorithms for this problem.