Applications of Data Mining in the Study of Acupuncture Acupoint Combination Patterns in Traditional medicine

Abstract

Data mining is increasingly recognized as a modern approach that enables the transformation of clinical experience and classical knowledge of Traditional Medicine into quantitative models that can be analyzed, validated, and optimized. This narrative review presents the theoretical foundations of data mining in traditional medicine research and introduces commonly used algorithms applied in acupuncture acupoint analysis, such as association rule mining and clustering analysis, together with illustrative examples to facilitate methodological understanding. Through this approach, the review aims to provide a foundational reference that supports researchers and clinicians in traditional medicine to effectively access data mining methods, thereby expanding their application in diagnosis and treatment, promoting individualized therapy, and advancing evidence-based acupuncture practice.