Research on Weighted Association Rules Based on the Traditional Chinese Medicine Jun Chen Zuo Shi Compatibility Theory^*

Objective: To address the planning and design requirements of clinical big data knowledge engineering in Traditional Chinese Medicine (TCM), this study focuses on the compatibility principles of TCM formulas, enhances association rule methodologies, and utilizes the clinical medication knowledge base for liver syndrome as a data source to investigate novel data mining techniques for understanding real-world medication practices. Method: Based on the theoretical framework of TCM, we developed a weighted association relationship algorithm that considers the compatibility of TCM ingredients. This algorithm was implemented in Python to create an improved association rule model. Result: By integrating the principles of TCM medication, we enhanced the existing model for mining association rules in TCM formulas, constructing a weighted model that reflects these principles. During the experimental phase, the improved model produced a greater number of frequent itemsets and rules compared to conventional models. The novel method, based on the weighted improved association rule model explored in this study, represents a significant advancement in analyzing TCM clinical medication patterns and holds substantial practical significance.