Conrad Albrecht, Jannik Schneider, et al.
CVPR 2025
Many problems can be reduced to the problem of combining multiple clusterings. In this paper, we first summarize different application scenarios of combining multiple clusterings and provide a new perspective of viewing the problem as a categorical clustering problem. We then show the connections between various consensus and clustering criteria and discuss the complexity results of the problem. Finally we propose a new method to determine the final clustering. Experiments on kinship terms and clustering popular music from heterogeneous feature sets show the effectiveness of combining multiple clusterings. © 2009 Springer Science+Business Media, LLC.
Conrad Albrecht, Jannik Schneider, et al.
CVPR 2025
Anurag Ajay, Seungwook Han, et al.
NeurIPS 2023
Ran Iwamoto, Kyoko Ohara
ICLC 2023
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence