Research Analysis on Clustering Ensemble Methods

Abstract

Clustering ensemble methods aim to improve the robustness, stability, and accuracy of clustering results by combining multiple individual clustering solutions. The idea is to leverage diverse clustering algorithms or variations of the same algorithm to capture different aspects of the underlying data structure. Ensemble methods can be particularly effective when dealing with complex datasets, noisy data, or when individual clustering algorithms are sensitive to specific initialization conditions.