灰色-周期外延模型的动态关联规则元规则挖掘  

Research of Mining Meta-association Rules for Dynamic Association Rule Based on Gray-periodic Extensional Model

作　　者：张忠林[1] 石皓尹 宋航[1] 

机构地区：[1]兰州交通大学电子与信息工程学院,兰州730070

出　　处：《计算机科学》2014年第4期239-243,共5页Computer Science

基　　金：国家自然科学基金资助项目(61163010);甘肃省科技支撑计划项目(1104GKCA016)资助

摘　　要：提出了一种把灰色-周期外延模型应用到动态关联规则元规则挖掘中并提高元规则预测精度的方法。该方法首先对动态关联规则元规则支持度计数建立GM(1,1)模型;然后对建模时产生的残差序列建立周期外延模型并且提取最优周期;其次将周期外延模型作为GM(1,1)模型的残差补偿,把GM(1,1)模型以及周期外延模型得到的结果进行叠加得到最终的预测模型。最后通过实验证明了该方法有较高的预测精度。A method applying Gray-Periodic extensional combimtorial model to Meta-rule mining for dynamic association rules to improve the prediction accuracy was proposed.In this method,firstly a GM（1,1） model is established by counting the support of the Meta-rule for dynamic association rules and then the residuals sequence generated during modeling is integrated to build the periodic extensional model and extract the optimal cycle.Secondly,the periodic extensional model is used as residual compensation for the GM（1,1） model.So the final prediction model is presented when superimposing the GM（1,1） model and results of the periodic extensional model together.At last,the results show that the method has higher prediction accuracy.

关 键 词：GM(1 1)模型 周期外延模型 动态关联规则 元规则 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]