data_ESMS

Ensemble clustering, which integrates multiple base clusterings to enhance robustness and accuracy, is commonly evaluated on over 10 benchmark datasets. These include 4 synthetic datasets (e.g., 3MC,atom,Tetra and Flame) designed to test algorithms on nonlinear separability and density variations. The 14 real-world datasets span diverse domains: classical UCI datasets (Iris,Heart,ISOLET,LM,MF,SPF,Australian,Breast Tissue,WDBC,seed and wine), scikit-learning datasets (USPS), MSRA,Coil20,These datasets collectively address challenges in cluster shape complexity, dimensionality, noise resilience, and scalability, enabling researchers to assess ensemble methods like CSPA, HGPA, and meta-clustering approaches using metrics such as ACC,NMI ,ARI and F1-score. Such evaluations drive advancements in applications like bioinformatics, customer segmentation, and anomaly detection.