摘要
研究中医高血压医案大数据集高效挖掘关联规则问题。中医医案数据量大、关联性强,针对传统的关联规则挖掘算法处理中医医案数据时存在效率低、收敛速度慢及漏报规则等问题,提出一种小生境技术和人工蜂群算法相结合的挖掘关联规则的方法。该方法通过惩罚函数设置支持度阈值,利用小生境技术执行小生境演化、融合算法,结合人工蜂群算法操作简单、鲁棒性强的优势搜索强关联规则,有效避免了算法早熟,解决了规则冗余。针对治疗高血压的中医医案进行了验证性实验,实验结果表明,相对于传统的关联规则挖掘算法,该方法在个体多样性及提取有效规则的效率上都有较大的提高,挖掘结果对高血压中医临床诊治具有一定的参考价值。
The paper deals with efficient mining association rules in large data sets of TCM clinical data of the hypertension. Aiming at the problems that TCM clinical data exist among a great deal of data and high association characteristics,which lead to the problem of low efficiency,slow convergence and omission rules while traditional methods mining association rules,a new combined method is proposed based on niche technology and artificial bee colony.The method designs the penalty function to set threshold,uses niche technology to finish evolution and integration and combines with the advantage of simple and robust of artificial bee colony which solves the problem of algorithm premature and redundancy rules.The medical treatment records of hypertension are verified by the experiments.Experimental results show that compared with tradition- al association rules mining method,the algorithm performs better in terms of diversity of population and discovering more effective association rules.The mining result has reference value in TCM treatment of the hypertension.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第36期226-229,233,共5页
Computer Engineering and Applications
基金
山东省高校科研发展计划项目(No.J11LG57)
关键词
关联规则
人工蜂群
小生境
高血压
association rules
artificial bee colony
niche
hypertension
