Clustering Validation

Clustering validation assesses the quality of clustering results, aiming to determine the optimal number of clusters and the effectiveness of the chosen clustering algorithm. Current research focuses on developing improved internal and external validity indices, addressing challenges posed by high-dimensional data and imbalanced clusters, and exploring the integration of clustering validation with automated machine learning (AutoML) frameworks. These advancements are crucial for ensuring the reliability and interpretability of clustering analyses across diverse applications, ranging from data visualization to complex scientific modeling.