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) 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 language | English (US) |
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Journal | Journal of Machine Learning Research |
Volume | 40 |
Issue number | 2015 |
State | Published - Jan 1 2015 |
Keywords
- Correlation Clustering
- Polynomial-Time Approximation Scheme
- Semi-Random Model
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Statistics and Probability
- Artificial Intelligence