Near-Optimal Correlation Clustering with Privacy

  title={Near-Optimal Correlation Clustering with Privacy},
  author={Vincent Cohen-Addad and Chenglin Fan and Silvio Lattanzi and Slobodan Mitrovi'c and Ashkan Norouzi-Fard and Nikos Parotsidis and Jakub Tarnawski},
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more. In the correlation clustering problem one receives as input a set of nodes and for each node a list of co-clustering preferences, and the goal is to output a clustering that minimizes the disagreement with the specified nodes’ preferences. In this paper, we introduce a simple and computationally efficient algorithm for the… 

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