基于SimRank的中药“效-效”相似关系挖掘  

‘Effect-Effect’Similarity Relation Mining in Traditional Chinese Medicine Based on SimRank

作　　者：田玲[1] 曾涛[1] 陈蓉[1] 袁楠[1] 于中华[1] 吴孟旭[2] 蒋永光[2] 

机构地区：[1]四川大学计算机学院,成都610064 [2]成都中医药大学药学院,成都610075

出　　处：《计算机工程》2008年第12期242-244,242,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60473071,90409007);高等学校博士学科点专项科研基金资助项目(20020610007);四川大学计算机学院青年基金资助项目

摘　　要：中药"效-效"关联分析是中医药研究中最基本也是最重要的问题,对药效判断具有重要意义。该文旨在利用数据挖掘技术,从中药方剂数据中自动挖掘"效-效"相似关系,自动归纳不同药效之间的相似度,提出了基于SimRank方法的"效-效"相似关系挖掘算法。中医专家对算法输出结果的大量验证表明,该算法具有较高的正确率,其中"优良"和"合理"共占70.568%。Analysis of ＇effect-effect＇ relations in Traditional Chinese Medicine（TCM） is one of the most fundamental and important issues for TCM research, which is of great significance for TCM prescription effect research. The paper intends to use data mining technology to automatically mine the similarity relations in TCM prescription data and induce degree of the similarity between different drug effects. For this reason, an algorithm of mining ＇effect-effect＇ similarity relations in TCM based on SimRank method is proposed in the paper. The results consulted by TCM experts show that the correct rate of the algorithm is comparatively high. Among them, ＇good＇ and ＇reasonable＇ have 70.568% totally.

关 键 词：数据挖掘 相似度 SimRank方法 中药 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]