Work

Correlation Clustering with Noisy Partial Information

Year: 2014

Type: preprint

Abstract: In this paper, we propose and study a semi-random model for the Correlation Clustering problem on arbitrary graphs G. We give two approximation algorithms for Correlation Clustering instances from this model. The first algorithm finds a solution of value $(1+ δ) optcost + O_δ(n\log^3 n)$ with high probability, where $optcost$ is the value of the optimal solution (for every $δ> 0$). The second algorithm finds the ground truth clustering with an arbitrarily small classification error $η$ (under some additional assumptions on the instance).

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1406.5667

Authors: Makarychev, Konstantin, Makarychev, Yury, Vijayaraghavan, Aravindan

Institutions: Courant Institute of Mathematical Sciences, Toyota Technological Institute, New York University

Language: English

Topic: Complexity and Algorithms in Graphs

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: RI: AF: Medium: Learning and Matrix Reconstruction with the Max-Norm and Related Factorization Norms, G6671297155, CAREER: Metric Geometry Techniques for Approximation Algorithms

OpenAlex ID: W2149125172

Cites: 19

Cited by: 10

Related to: 20