实时数据库中的知识发现后处理研究

Research on Post-processing of Knowledge Discovery in Real-time Database

实时数据库的数据挖掘所得到的知识存在着时间上的滞后性.针对这一问题,研究了关联规则参数的演化规律并利用其实现知识发现后处理中知识的自动评价.用组态和组态空间来描述关联规则的实时变化,并在此基础上给出了关联规则的参数演化定理,通过实例验证其有效性.该研究结果可拓广到其他知识类型的参数演化与评价中.

The knowledge acquired from data mining in real-time database is always time-lag. In this paper,we study on evolvement of parameters of association rules and the auto-evaluation of post-processing. The change of parameters of association rules was described by configuration and configuration space, based on which we present parameter evolvement theorem, we test the effectiveness of our idea by experiments. The results of the paper can also be applied to other evolvement of parameters.