预测模型自学教程：从数据清洗到模型外部验证（R 语言）
This C++ project implements and evaluates the performance of K-Means clustering using internal validation metrics such as the Calinski-Harabasz (CH) Index and Davies-Bouldin (DB) Score. It also includes preprocessing steps like data normalization and supports running multiple randomized experiments to find optimal cluster configurations.
Code repository for the manuscript: Violent victimisation after adolescent traumatic brain injury: development and validation of a clinical prediction model (published in Injury Prevention)
RFM-based customer segmentation using K-Means, GMM, Hierarchical Clustering, PAM, and DBSCAN with internal cluster validation on Online Retail II data.
CompSil: Composite Silhouette for Cluster-Count Selection
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