基于小波变换的动态关联规则元规则GM(1,1)挖掘

计算机科学 ›› 2013, Vol. 40 ›› Issue (5): 209-212.

基于小波变换的动态关联规则元规则GM(1,1)挖掘

张忠林,许凡

兰州交通大学电子与信息工程学院兰州730070;兰州交通大学电子与信息工程学院兰州730070

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然科学基金项目(61163010),甘肃省科技支撑计划项目(1011GKCA040)资助

Meta-association Rule Mining for Dynamic Association Rule Used GM(1,1) Based on Wavelet Transform

ZHANG Zhong-lin and XU Fan

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 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并提高规则预测精度的方法。该方法首先利用小波变换技术对挖掘出的动态关联规则元规则支持度计数进行变换,然后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,并利用这两部分别进行单支重构,随后利用GM(1,1)对重构的两部分进行预测,从而得到最后的预测结果,最后通过实验证明了该方法具有较高的预测精度。

关键词: 小波变换,动态关联规则,元规则,单支重构,GM(1,1)

Abstract: 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．First,it uses Daubechies wavelet to transpose the support for the technology of the meta-rule in dynamic association rules．And then it extracts the approximate part and detail part according to the multi-resolution characteristics of wavelet transform．Finally by single reconstructing the two parts the GM(1,1) can be used to forecast the reconstructed two parts to get the final prediction results．The last experiment gives higher prediction accuracy.

Key words: Wavelet transform,Dynamic association rules,Meta-association rule,Single reconstructed,GM(1,1)

张忠林,许凡. 基于小波变换的动态关联规则元规则GM(1,1)挖掘[J]. 计算机科学, 2013, 40(5): 209-212. https://doi.org/

ZHANG Zhong-lin and XU Fan. Meta-association Rule Mining for Dynamic Association Rule Used GM(1,1) Based on Wavelet Transform[J]. Computer Science, 2013, 40(5): 209-212. https://doi.org/

参考文献

[1] Liu Jin-feng,Rong gang．Mining dynamic association rules in databases[C]∥Xi’an Proceedings of International Conference on Computtional Intelligences and Security 2005.Xi’an,2005:688-955
[2] 荣冈,刘进锋,顾海杰．数据库中动态关联规则的挖掘[J].控制理论与应用,2007,24(1):129-133
[3] 沈斌,姚敏.一种新的动态关联规则及其挖掘算法[J]．控制与决策,2009,4(9):1310-1315
[4] 刘俊,张忠林,谢彦峰,等.基于时间序列模型的关联规则元规则挖掘[J].计算机工程,2009,15(35):94-96
[5] 胡俊胡,玉清,肖忠卿.基于小波变换的网络流量预测模型[J].计算机工程,2008,4(19):112-114,9
[6] 吴朝阳.小波变换和GM-ARMA组合模型的股指预测[J].智能系统学报,2011,6(3):279-282
[7] 白翔宇,叶新铭,蒋海.基于小波变换与自回归模型的网络流量预测[J].计算机科学,2007,34(7):47-54
[8] 佟伟明,李一军,单永正.基于小波分析的时间序列数据挖掘[J].计算机工程,2008,4(1):26-28
[9] Zhang Yi,Wei Yong,Zhou Ping.Improved Approach of GrayDerivative in GM(1,1) Model [J].The Journal of Grey System,2006,6(10):160-162
[10] Sun Yan-na.Optimization of Grey Derivative in GM(1,1) Based on the Discrete Exponential Sequence[C]∥Proceeding of the 2nd International Symposium on Information Processing(ISTP 2009)．Huangshan,P.R.China,2009:313-315
[11] Yang Jiang-tian.Multivariable trend analysis using grey model for machinery condition monitoring[C]∥Eleventh World Congress in Mechanism and Machine Science．2004:2188-2191
[12] 张华,任若恩.基于小波分解和残差GM(1,1)—AR的非平稳时间序列预测[J]．系统工程理论与实践,2010,0(6):1016-1020
[13] 刘思峰,党耀国,方志耕,等．灰色系统理论及其应用[M]．北京:科学出版社,2004:142-146
[14] 刘思峰,邓聚龙.GM(1．1)模型的适用范围[J]．系统工程理论及实践,2000,20(5):121-124
[15] 张忠林,曾庆飞,许凡.动态关联规则的趋势度挖掘方法[J].计算机应用,2012,2(1):196-198

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