Ensemble Clustering

Ensemble clustering aims to improve the accuracy and robustness of clustering results by combining the outputs of multiple individual clustering algorithms. Current research focuses on developing novel ensemble methods, such as those incorporating deep learning architectures (e.g., graph neural networks) or Bayesian approaches, and on addressing challenges like computational complexity and the selection of optimal base clustering algorithms and hyperparameters. These advancements enhance the reliability and applicability of clustering techniques across diverse domains, including spatiotemporal data analysis, federated learning, and image processing, leading to more robust and interpretable insights from complex datasets.