Data and Code: Traditional and Non-Traditional Clustering Techniques for Identifying Chrononutrition Patterns

Anonymized data (n=459) and reproducible R scripts for the paper 'Traditional and Non-Traditional Clustering Techniques for Identifying Chrononutrition Patterns'. This repository implements 4 clustering methods (K-means, Hierarchical Ward, Gaussian Mixture Models, and Spectral Clustering) applied to meal timing data from Mexican university students. The analysis identifies chrononutrition patterns based on breakfast, lunch, and dinner timing.本数据集包含对应论文《用于识别时间营养学（Chrononutrition）模式的传统与非传统聚类技术》的匿名化数据（样本量n=459）与可复现R脚本。本代码仓库实现了4种聚类方法，即K均值聚类（K-means）、分层沃德聚类（Hierarchical Ward）、高斯混合模型（Gaussian Mixture Models）与谱聚类（Spectral Clustering），并将其应用于墨西哥大学生的用餐时间数据。本分析基于早餐、午餐及晚餐的进食时间，识别时间营养学模式。