Clustering ensembles

Clustering ensembles combine multiple clustering results to produce a more accurate and stable grouping of data. Imagine sorting objects in different ways—by size, color, or shape—and then blending these methods to find the best overall grouping. By integrating various clustering outcomes, ensemble methods reduce errors and reveal clearer patterns. This approach leverages the strengths of individual algorithms while minimizing their weaknesses, resulting in a consensus that is more robust and reliable for understanding complex data structures.