An unsupervised partition method based on association delineated revised mutual information

BACKGROUND: The syndrome is the basic pathological unit and the key concept in traditional Chinese medicine (TCM) and the herbal remedy is prescribed according to the syndrome a patient catches. Nevertheless, few studies are dedicated to investigate the number of syndromes and what these syndromes are. Correlative measure based on mutual information can measure arbitrary statistical dependences between discrete and continuous variables. RESULTS: We presented a revised version of mutual information to discriminate positive and negative association. The entropy partition method self-organizedly discovers the effective patterns in patient data and rat data. The super-additivity of cluster by mutual information is proved and N-class association concept is introduced in our model to reduce computational complexity. Validation of the algorithm is performed by using the patient data and its diagnostic data. The partition results of patient data indicate that the algorithm achieves a high sensitivity with 96.48% and each classified pattern is of clinical significance. The partition results of rat data show the inherent relationship between vascular endothelial function related parameters and neuro-endocrine-immune (NEI) network related parameters. CONCLUSION: Therefore, we conclude that the algorithm provides an excellent solution to patients and rats data problem in the context of traditional Chinese medicine.