Clustering Ensemble

Clustering Ensemble is an ensemble learning method that improves the stability and accuracy of clustering by integrating the results of multiple base clustering algorithms. Its objective is to leverage the strengths of different clustering algorithms, reduce the limitations of a single algorithm, and thereby obtain more robust clustering outcomes. This method has significant application value in fields such as data mining, machine learning, and pattern recognition, especially when dealing with large-scale and high-dimensional data, as it can notably enhance clustering performance.