A 4-Approximation Algorithm for Min Max Correlation Clustering.

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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 languageEnglish
Title of host publicationProceedings of The 27th International Conference on Artificial Intelligence and Statistics
Pages1945-1953
Number of pages9
Publication statusPublished - 2024
Peer-reviewedYes

Publication series

SeriesProceedings of Machine Learning Research
Volume238

External IDs

Scopus 85193764037
ORCID /0000-0001-5036-9162/work/161407133
dblp journals/corr/abs-2310-09196

Keywords