关键词: 数据挖掘;动态关联规则;趋势度;灰色-Markov（GM）（1, 1）模型;Markov链;预测

Abstract: According to the feature that dynamic trend degree of association rules can change over time, this paper puts forward a method of applying the grey-Markov model to tendency measure mining in dynamic association rules on the basis of analyzing the original definition and the dynamic correlation trend prediction model. The trend of the rules can be obtained with the definition of the tendency measure mining. For those rules which do not meet the threshold value of trend, the method uses grey-Markov model to forecast their support degree counting sequences. And then the predicted data is joined into Meta-rule’s support degree counting sequences to get the new trend of the rules which should be determined whether it can meet the threshold requirements. The method is not only proved to be valid but also improving the accuracy and efficiency of mining so that dynamic association rule mining can get more comprehensive and more accurate results through analyzing a case.

Key words: data mining, dynamic association rules, tendency measure, Grey-Markov（GM）（1, 1）model, Markov chain, forecast