Abstract: Rough Set (RS) theory is an effective approach of imprecision, vagueness and incompleteness in classification analysis and knowledge discovery. Rough set based on boundary region was dynamically analyzed from the coarser degree of granularity. Dynamic quotient granularity was defined by the principle of attribute connection, and a new attribute reduction algorithm based on the coarser degree of granularity principle was proposed. The optimal reduction set can be selected from all reduction set with algorithm for dynamic quotient granularity. It abandons the tradition to ask the core first, and then chooses the optimal reduction set. The validity of proposed granularity computing algorithm is proved by the application of practical database. Moreover, it can be used for granularity computation of knowledge.

摘要： 粗糙集理论在对不精确、不确定和不完全的数据进行分类分析和知识获取中具有突出的优势。从粒度粗细的角度动态分析了粗糙集的边界域,结合属性关联的理论定义了动态粒度商的概念。依据粒度粗细的理论,提出了一种新的属性约简算法。采用动态粒度商法选择最优归约集,抛弃了传统的先求核心,再选择最优归约集的算法。实例研究证明提出的粒度计算方法是可靠有效的,为进一步研究知识的粒度计算提供了可行的方法。

关键词: 粗糙集, 粒度计算, 决策系统, 粒度商, 动态粒度商, Rough Set (RS), granular computing, decision system, Quotient Granularity (QG), dynamic QG