基于小波变换的动态关联规则元规则挖掘方法

针对动态关联规则元规则挖掘中规则预测精度不高的问题，提出了一种把小波变换应用到动态关联规则元规则挖掘中以提高规则预测精度的方法。首先利用Daubechies小波对挖掘出的动态关联规则元规则支持度计数进行变换；其次通过小波变换的多分辨率特点提取出近似部分和细节部分；然后利用两部分进行曲线的误差计算与小波变换分解层次的选择控制，用过滤的近似信号进行逆变换和曲线拟合进而进行规则预测；最后用预测的数据进行验证证明其预测精度达到90%以上。实验结果表明所提方法能更好地反映规则随时间变化的动态信息和变化趋势，从而使动态关联规则挖掘在合理的元规则指导下得到更精确的结果。

Meta-rule mining method for dynamic association rules based on wavelet transform

Concerning the problem that the forecast accuracy of the meta-rule mining in dynamic association rules is not high,this paper put forward a method for applying wavelet transform to meta-rule mining in dynamic association rules to improve the forecast accuracy of the rules.Firstly,the Daubechies wavelet was used to transform the support count of the dynamic meta-association rules.Secondly,the approximate part and detailed part could be extracted according to the multi-resolution characteristics of wavelet transform.And then the curve error calculation and selection control of wavelet decomposition level could be processed by using the two parts followed by inverse transforming and curve fitting by using the filtered approximate signal to conduct the predictions.The experimental results prove that the forecast precision is more than 90% by using the last forecast data.Finally,it turns out that the proposed method can better reflect the dynamic information and trends of the rules changing with time so as to get more accurate results of dynamic association rules with the guidance of reasonable meta-rules.