Silhouette Coefficient

The silhouette coefficient is a widely used metric for evaluating the quality of clustering results, assigning a score to each data point reflecting its similarity to its own cluster compared to others. Current research focuses on improving its robustness, particularly addressing challenges posed by imbalanced datasets and high-dimensional data, leading to the development of alternative averaging strategies and adaptations for distributed computing environments. These advancements enhance the applicability of the silhouette coefficient in various fields, including gait recognition, feature selection, and the analysis of multidimensional projections, ultimately improving the reliability and efficiency of clustering algorithms.