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Reduction method for concept lattices based on rough set theory and its application

机译:基于粗糙集理论的概念格约简方法及其应用

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摘要

Rough set theory and formal concept analysis are two complementary mathematical tools for data analysis. In this paper, we study the reduction of the concept lattices based on rough set theory and propose two kinds of reduction methods for the above concept lattices. First, we present the sufficient and necessary conditions for justifying whether an attribute and an object are dispensable or indispensable in the above concept lattices. Based on the above justifying conditions, we propose a kind of multi-step attribute reduction method and object reduction method for the concept lattices, respectively. Then, on the basis of the defined discernibility functions of the concept lattices, we propose a kind of single-step reduction method for the concept lattices. Additionally, the relations between the attribute reduction of the concept lattices in FCA and the attribute reduction of the information system in rough set theory are discussed in detail. At last, we apply the above multi-step attribute reduction method for the concept lattices based on rough set theory to the reduction of the redundant premises of the multiple rules used in the job shop scheduling problem. The numerical computational results show that the reduction method for the concept lattices is effective in the reduction of the multiple rules.
机译:粗糙集理论和形式概念分析是用于数据分析的两个互补数学工具。本文基于粗糙集理论研究概念格的约简,提出了上述概念格的两种约简方法。首先,我们提供了充分必要的条件,以证明在上述概念格中属性和对象是可有可无的。基于上述证明条件,我们提出了一种概念格的多步属性约简方法和对象约简方法。然后,根据定义的概念格区分函数,提出了一种概念格的单步归约方法。另外,详细讨论了FCA中概念格的属性约简和粗糙集理论中信息系统的属性约简之间的关系。最后,我们将上述基于粗糙集理论的概念格多步属性约简方法应用于作业车间调度问题中所使用的多个规则的冗余前提的约简。数值计算结果表明,概念格的约简方法对多规则的约简有效。

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