Consensus Clustering

Consensus clustering avoids relying directly on the output of a single algorithm or initialization. Instead, it compares multiple clustering solutions and extracts shared patterns. This is especially useful in datasets where the number of clusters is uncertain or results are sensitive to initialization. It combines exploratory learning with stability analysis, often leading to more reliable segmentation structures.