Centroid Based Clustering

Centroid-based clustering aims to group data points into clusters based on their proximity to central points, or centroids. Current research focuses on improving the robustness and efficiency of algorithms like K-means, addressing limitations such as sensitivity to initial conditions, outlier influence, and the need for pre-specifying the number of clusters. This involves exploring alternative centroid definitions (e.g., medians), integrating manifold learning techniques, and developing methods that handle high-dimensional data and non-spherical clusters more effectively. These advancements have implications for various applications, including data summarization, vehicle routing optimization, and network analysis.