
The authors propose the cluster LP as a strong linear program that unifies previous relaxations for the Correlation Clustering problem, and present improved approximation algorithms and hardness results based on this new framework.


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approximation-algorithms-for-the-correlation-clustering-problem


Approximation Algorithms for the Correlation Clustering Problem


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We present a novel combinatorial algorithm that achieves a 2 - 2/13 < 1.847-approximation for the Correlation Clustering problem, substantially improving over the classic 3-approximation. Our algorithm uses a local search approach combined with a systematic "flipping" technique to escape bad local minima.


efficient-combinatorial-algorithm-for-correlation-clustering-with-improved-approximation-guarantee


Efficient Combinatorial Algorithm for Correlation Clustering with Improved Approximation Guarantee
