Mining dynamic association rules in databases
数据库中动态关联规则的挖掘

Association rules may discover the relations between variables, but are unable to reflect the variation between relations. Consequently, dynamic association rule is introduced in this paper. In our method, the entire database is divided into a series of subsets in time field, and each rule from a subset has a measure of support and confidence. As a result, there are a vector of supports and a vector of confidences for each rule. It not only helps us discover the rule variation with time by analyzing the two vectors, but also predicts the future of a rule. Two algorithms for mining dynamic association rule are proposed in this paper, and a comparison of such two algorithms is also made. Subsequently, histograms and time series are described as ways for analyzing the two vectors. Finally, the effects of dynamic association rule are shown in an instance.