A 4-Approximation Algorithm for Min Max Correlation Clustering.
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
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
| Originalsprache | Englisch |
|---|---|
| Titel | Proceedings of The 27th International Conference on Artificial Intelligence and Statistics |
| Seiten | 1945-1953 |
| Seitenumfang | 9 |
| Publikationsstatus | Veröffentlicht - 2024 |
| Peer-Review-Status | Ja |
Publikationsreihe
| Reihe | Proceedings of Machine Learning Research |
|---|---|
| Band | 238 |
Externe IDs
| Scopus | 85193764037 |
|---|---|
| ORCID | /0000-0001-5036-9162/work/161407133 |
| dblp | journals/corr/abs-2310-09196 |