Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (14): 135-137.DOI: 10.3778/j.issn.1002-8331.2010.14.040

• 数据库、信号与信息处理 • Previous Articles     Next Articles

Auto-regression Markov model application in mining of dynamic association rule

ZHANG Zhong-lin,LIU Jun,XIE Yan-feng   

  1. School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China
  • Received:2008-11-03 Revised:2009-01-19 Online:2010-05-11 Published:2010-05-11
  • Contact: ZHANG Zhong-lin


张忠林,刘 俊,谢彦峰   

  1. 兰州交通大学 电子与信息工程学院,兰州 730070
  • 通讯作者: 张忠林

Abstract: According to the characteristics of the rules change over time,it establishes the change trend analysis and forecasting model about support and confidence of the meta-association rule.By increasing the support and confidence vector evaluation of the rules,it gives the dynamic association rules of Meta-association formal definition.Using auto-regression Markov model of mining dynamic association rules of meta-association rules,it proves the effectiveness of this method.

Key words: dynamic association rule, meta-association rule mining, auto-regression model, Markov chains

摘要: 针对规则随着时间变化的特点,为规则建立元规则对其支持度和置信度变化趋势的分析和预测模型。通过增加支持度向量和置信度向量这两种规则评价指标,给出了动态关联规则元规则的形式化定义。利用自回归Markov模型对动态关联规则的元规则进行了挖掘,并通过实例证明了该方法的有效性。

关键词: 动态关联规则, 元规则挖掘, 自回归模型, Markov链

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