Consistency Based Rules Mining on Sparse and Diverse TCM Sub-health Diagnosis Data

摘要：

This paper proposes a method of consistency based rules mining on sparse and diverse data set derived from the sub-health diagnosis of TCM doctors, so as to realize the automatic inference of individuals sub-health state and their corresponding TCM syndrome. Because of the datas bias given by doctors, a consistency detection algorithm to find out the feature sets that can fit the doctors diagnosis is presented, and the rule mining algorithm is instructed by it to forecast the sub-health state. Derivation accuracies before and after using the consistency detection algorithm are given by our experiments. The performance of the consistency detection algorithm is evaluated, and the limitation is analyzed.

关键词： TCM Sub-health Consistency Detection Rule Mining

作者: Feng Guo Ying Dai Ying Lin Shaozi Li Kenzo Ito

作者单位: Fujian Key Lab of the Brain-like Intelligent Systems of Xiamen University Xiamen, China Faculty of Software and Info.Science of Iwate Pref.University Iwate, Japan Cognitive Science department of Xiamen University Xiamen, China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 896-901

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）