Correlation clustering with noisy partial information

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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) opt-cost+Oδ (n log3 n) with high probability, where opt-cost 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 ν (under some additional assumptions on the instance).

Original languageEnglish (US)
JournalJournal of Machine Learning Research
Issue number2015
StatePublished - Jan 1 2015


  • Correlation Clustering
  • Polynomial-Time Approximation Scheme
  • Semi-Random Model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Statistics and Probability
  • Artificial Intelligence


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