ABSTRACT
Through several thousand years’ inheritance and development by Chinese people, traditional Chinese medicine (TCM) has formed its unique theoretic system, whose efficacy has been widely accepted. However, because TCM theory focuses on the relationships among syndromes, treatment and efficacy, instead of the cause-and-effect relationship explored by modern natural science, the scientificity of TCM has always been questioned. In recent years, because virtual-world clinical research mode and the concept of “big data” were emphasized, increasing researchers began to put their research emphasis on the correlativity between intervening measures of diseases and outcome indicators. This change and the advancement of computer data mining and analysis technology, bring great opportunities for the further development of TCM theory and practice. This article concluded data mining technology used in TCM diagnosis and treatment in recent years, such as clustering analysis, decision tree, Bayesian network, neural network and multi-instance learning, which showed how to apply these methods to reveal rules of TCM diagnosis and treatment from a large number of TCM syndrome data, find knowledge hidden in data, and show TCM effectiveness supported by data.