Consensus Clustering

Consensus clustering aims to improve the robustness and accuracy of clustering results by aggregating multiple individual clusterings of the same dataset. Current research focuses on developing efficient algorithms, such as those based on k-means and evidence accumulation, and incorporating deep learning techniques to learn better representations for improved consensus. This approach is proving valuable in diverse applications, including image analysis and network analysis, by mitigating the limitations of individual clustering methods and providing more reliable and interpretable results.