The Associating Rules of Mongolian Medical Prescriptions Based on Reduced Binary Matrix
10.11842/wst.2017.02.028
- VernacularTitle:基于精简二元矩阵的蒙医方剂关联规则挖掘
- Author:
Chunsheng ZHANG
;
Ya TU
;
Yan LI
- Keywords:
Binary matrix;
Mongolian medical prescriptions;
associating rules;
data mining
- From:
World Science and Technology-Modernization of Traditional Chinese Medicine
2017;19(2):365-369
- CountryChina
- Language:Chinese
-
Abstract:
The associating rules have been widely used in traditional Chinese medical (TCM) prescription data mining research work,while Mongolian medical prescription data mining work hardly carried out.In this study,Apriori algorithm based on reduced binary matrix was adopted for the data mining of Mongolian medical prescriptions with the provision of the decision support for research and development of new drugs.Twenty-seven Mongolian medical prescriptions for He Yi disease were involved in the data mining with Apriori algorithm based on reduced binary matrix.It was found that Aquilaria agallocha,Myristica fragrans,Zhen He Yi,or Aquilaria agallocha combined with Myristica fragrans,or Zhen He Yi combined with Myristica fragrans were determined as the Mongolian drugs frequently used in He Yi disease,with 80％ confidence level and 60％ support,and two algorithms were output,[Myristica fragrans--＞Zhen He Yi,0.83] and [Aquilaria agallocha--＞Myristica fragrans,1.00].In conclusion,some potential associating rules and drugs with high frequency in the Mongolian medical prescriptions can be fast mined using Apriori algorithm based on reduced binary matrix,providing a new way for unveiling the medication rules of Mongolian medical prescriptions.
