  There has been a continuing demand for traditional and complementary medicine
worldwide. A fundamental and important topic in Traditional Chinese Medicine
(TCM) is to optimize the prescription and to detect herb regularities from TCM
data. In this paper, we propose a novel clustering model to solve this general
problem of herb categorization, a pivotal task of prescription optimization and
herb regularities. The model utilizes Random Walks method, Bayesian rules and
Expectation Maximization(EM) models to complete a clustering analysis
effectively on a heterogeneous information network. We performed extensive
experiments on the real-world datasets and compared our method with other
algorithms and experts. Experimental results have demonstrated the
effectiveness of the proposed model for discovering useful categorization of
herbs and its potential clinical manifestations.
