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On Invariance of Concept Stability for Attribute Reduction in Concept Lattice

机译:论概念稳定性的概念稳定性的不变性

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Formal Concept Analysis (FCA) methodology, as an efficient knowledge representation and knowledge discovery tool and has been widely used in various fields, such as data mining, expert systems, and others. Knowledge reduction is an essential issue for knowledge discovery. This paper focuses on attribute reduction in FCA and explores the internal relation between concept stability and attribute reduction. By observing the concept stability of concepts in original concept lattice and reduced concept lattice, a theorem about the invariance of concept stability for attribute reduction in concept lattice is presented and proved mathematically. It is believed that the proposed theorem provides a novel solution for quick attribute reduction and benefit for other social system applications.
机译:正式概念分析(FCA)方法,作为一种有效的知识表示和知识发现工具,并已广泛用于各种领域,例如数据挖掘,专家系统和其他领域。 知识减少是知识发现的重要问题。 本文侧重于FCA的属性减少,并探讨了概念稳定性与属性之间的内部关系。 通过观察原始概念晶格和减少概念格的概念概念稳定性,介绍了概念稳定性的概念稳定性的定理,并在数学上证明并证明了概念晶格中的属性减少。 据信,所提出的定理为其他社会系统应用提供了一种新的解决方案,以便快速减少和效益。

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