“Dual-Level Clustering Ensemble Algorithm with Adaptive Member Selection and Dynamic Evidence Theory Improves Consensus and Robustness”

https://pubmed.ncbi.nlm.nih.gov/38114636

This study proposes a dual-level clustering ensemble algorithm with adaptive member selection, modified base clustering consensus methods, and a dynamic evidence theory for fusing ensemble results, which demonstrates improved consensus ability and robustness compared to existing algorithms.