Using Hierarchical Clustering Algorithm to Detect Community Structure in Traditional Chinese Medicine Formula Network

Traditional Chinese medicine (TCM) is a holistic medical approach and the formula's composition discipline is still a mystery. Detecting a formula's structure and herb communities/clusters in TCM Formula networks (TCMF) is a mainly existing problem in data mining of the data sets. In this paper, we devise a novel community similarity calculating method in the process of clustering, which is called Random Walk Hierarchical Clustering (RWHC) algorithm, to identify herb communities by using clustering algorithms based on the formula network of atrophic lung disease. And we also use classic NG modularity function to evaluate the experimental results. The studies suggest that the TCM network clustering approach provides a new research paradigm for mining TCM data from an experience-based medicine, will accelerate TCM drug discovery, and also improve current drug discovery strategies.