【摘要】 粗糙集方法是一种处理不确定或模糊知识的重要工具。传统的粗糙集模型对最简规则集的研究都是针对静态数据的,对于动态数据却显得无能为力。但在实际应用中,数据库中的数据往往是动态变化的,因此,对规则约简的增量式算法的研究是知识发现领域所急需解决的问题之一。文章给出了一种基于粗糙集和决策树的增量式规则约简算法,并与传统算法和RRIA算法进行了对比分析,实验结果表明该算法的方法和效果更好。更多还原

【Abstract】 The rough set approach is an important tool to deal with uncertain or vague knowledge.Traditional rough set model on research of the minimal rule sets is based on static data.It cannot handle incremental data.However,the data in database is always incremental.Therefore incremental reduction of rules is a topic of general interest in the field of knowledge discovery.In this paper,an incremental learning method based on rough set theory and decision trees techniques is proposed.Then it is compared with classical and RRIA algorithm.The results show the method and effect of the algorithm are better.更多还原