基于概念格的分布式约简理论框架

为了缩减知识推理空间,提高分布式环境下知识处理的效率,提出分布式概念格属性约简的理论框架.基于粗糙集理论的思想,从子形式背景和全局形式背景的角度,刻画了核心属性、相对必要属性和绝对不必要属性的属性特征,给出属性约简的判定定理.在此基础上,给出概念格的分布式属性约简方法:首先,使用现有的约简方法分别计算各子形式背景的约简,然后,逐一利用各子背景的约简,通过合并计算得到全局形式背景的约简.给出了算法的实现并用实例验证了它的有效性.分布式约简有效避免了使用现有方法而引起的数据安全和网络通信等问题,提高了约简的计算效率.

Theoretical framework for distributed reduction in concept lattice

In order to reduce knowledge reasoning space and improve knowledge processing efficiency, a framework of distributed attribute reduction in concept lattices is presented. By employing the idea similar to that of the rough set,the characterization of core attributes,dispensable attributes and unnecessary attributes are described from the point of view of local formal contexts and virtual global contexts.A determinant theorem of attribute reduction is derived.Based on these results,an approach for distributed attribute reduction is presented.It first performs reduction independently on each local context using the existing approaches,and then local reducts are merged to compute reducts of global contexts.An algorithm implementation is provided and its effectiveness is validated.The distributed reduction algorithm facilitates not only improving computation efficiency but also avoiding the problems caused by the existing approaches,such as data privacy and communication overhead.