A 4-Approximation Algorithm for Min Max Correlation Clustering.
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
We introduce a lower bounding technique for the min max correlation clustering problem and, based on this technique, a combinatorial 4-approximation algorithm for complete graphs. This improves upon the previous best known approximation guarantees of 5, using a linear program formulation (Kalhan et al., 2019), and 40, for a combinatorial algorithm (Davies et al., 2023). We extend this algorithm by a greedy joining heuristic and show empirically that it improves the state of the art in solution quality and runtime on several benchmark datasets.
Details
Original language | English |
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Title of host publication | Proceedings of The 27th International Conference on Artificial Intelligence and Statistics |
Pages | 1945-1953 |
Number of pages | 9 |
Publication status | Published - 2024 |
Peer-reviewed | Yes |
Publication series
Series | Proceedings of Machine Learning Research |
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Volume | 238 |
External IDs
Scopus | 85193764037 |
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ORCID | /0000-0001-5036-9162/work/161407133 |
dblp | journals/corr/abs-2310-09196 |