改进的增量式关联规则维护算法

挖掘关联规则是数据挖掘研究的一个重要方面，而维护已发现的关联规则同样是重要的。在分析现有的关联规则算法IUA的基础上，指出了该算法的不足和错误之处，并加以改正，进而提出了一种改进的增量式更新算法EIUA。EIUA算法解决了在数据库D不变的情况下，当最小支持度和最小置信度二阈值发生变化时如何高效更新关联规则的问题。实验分析表明了新算法的有效性和优越性。

An improved incremental algorithm for maintaining discovered association rules

Mining association rules is a major aspect of data mining research, and maintaining discovered association rules is of equal importance. In this thesis, this paper analyzes a previously proposed algorithm IUA and point out its disadvantages and errors and manage to correct these errors. Furthermore we have proposed an improved incremental maintaining algorithm EIUA. Assuming that database D is not updated, ELIJA has solved the problem of how to maintain discovered association rules efficiently when the two thresholds, minimum support and confidence, change. The experiments have shown the availability and superiority of the new algorithm.