Correlation Clustering with Constrained Cluster Sizes and Extended Weights Bounds

TL;DR

This paper introduces correlation clustering with size constraints and extended weight bounds, providing new algorithms with constant approximation guarantees for these complex clustering scenarios.

Contribution

It presents the first study of correlation clustering with bounded cluster sizes and extends approximation guarantees to broader weight regimes.

Findings

Established polynomial-time algorithms with constant approximation for extended weight bounds.

Introduced the problem of correlation clustering with size constraints.

Applied extended weight analysis to bounded cluster size clustering.

Abstract

We consider the problem of correlation clustering on graphs with constraints on both the cluster sizes and the positive and negative weights of edges. Our contributions are twofold: First, we introduce the problem of correlation clustering with bounded cluster sizes. Second, we extend the regime of weight values for which the clustering may be performed with constant approximation guarantees in polynomial time and apply the results to the bounded cluster size problem.