RESUMEN
To present a data processing method of traditional Chinese medicine based on Apriori algorithm to improve the efficiency of data mining and ensure the accuracy of the knowledge or conclusion in the data mining. The importance of data preprocessing in data mining was analyzed, along with the characteristics of TCM data and the requirements of Apriori algorithm for mining data. Some new functions were formed with considerations on the exam-ples. The data preprocessing was explored from the aspects of terminology standardization, eliminating unqualified data, structured prescription data, data sorting and etc. The new functions were simple and easy to operate, and the preprocessed data made the efficiency of TCM data mining enhanced greatly. The preprocessing method based on Apriori algorithm for TCM data facilities the TCM data mining.
RESUMEN
As the fitting value of Michaelis-Menten pharmacokinetic parameters K_m and V_m of accurate linear regress (ALR) or improved Hanes-Woolf method has some deviation, a optimizing method of K_m and V_m was used in this paper. The result of ALR or improved HanesWoolf method was taken as the primary value of parameters(V_m and K_m,). The method combined Runge-Kutta algorithm with program solution in Excel software (RK-PS) was used to minimum weighting residual square sum [∑ (c-c~*)2/c] of concentration. The primary value of parameters was optimized. The RK-PS method was better than ALR method and improved Hanes-Woolf method in two examples.