摘要
关联规则挖掘主要用于发现事务数据集中项与项之间的关系,现有的关联规则挖掘算法多是挖掘一种静态的关联规则,实际上规则随着时间的推移可能会有很大变化,为规则建立元规则对其支持度和置信度变化趋势进行分析和预测,有利于进一步指导挖掘和决策。通过一个实例介绍了一种基于马尔可夫模型的预测和分析的元规则的具体方法,并通过与其他方法的对比说明它是一个合理的模型。
The association rule mining aims at the relationships between the items of the transaction data sets.Many algorithms for mining association rules are considered as static ones.In fact,it is possible that a rule will change greatly with time,so it is of great help to do further study and make a strategic decision to set up a meta-association rule for the rule,analyze and predict the change tendency of its support value and confidence value.By means of an example,this paper analyzes the usual mining process of the meta-association rules by Markov model.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第16期152-154,共3页
Computer Engineering and Applications
关键词
关联规则
元规则
马尔可夫链
预测
association rule
recta-association rule
Markov chains
forecast
