Correlation Clustering - Minimizing Disagreements on Arbitrary Weighted Graphs

Dotan Emanuel, Amos Fiat

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

We solve several open problems concerning the correlation clustering problem introduced by Bansal, Blum and Chawla [1]. We give an equivalence argument between these problems and the multicut problem. This implies an O(log n) approximation algorithm for minimizing disagreements on weighted and unweighted graphs. The equivalence also implies that these problems are APX-hard and suggests that improving the upper bound to obtain a constant factor approximation is non trivial. We also briefly discuss some seemingly interesting applications of correlation clustering.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsGiuseppe di Battista, Uri Zwick
PublisherSpringer Verlag
Pages208-220
Number of pages13
ISBN (Print)3540200649, 9783540200642
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2832
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Dive into the research topics of 'Correlation Clustering - Minimizing Disagreements on Arbitrary Weighted Graphs'. Together they form a unique fingerprint.

Cite this