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Incomplete Concept Lattice Data Analytical Method Research Based on Rough Set Theory

机译:基于粗糙集理论的不完整概念晶格数据分析方法研究

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Concept lattice and rough set are powerful tools for data analyzing and processing, has been successfully applied to many fields. However, the decision information is incomplete in many information systems. In this paper, the definition of incomplete concept lattice has been proposed, and some relation established between imperfect concept lattice and rough set. As is very important that the paper gives a new attributes reduction algorithm about incomplete concept lattice aims at the matter of the inefficient of reduction strategy based on discernibility matrix. Comparing with the attributes reduction for incomplete concept lattice which based on discernibility matrix, this reduction algorithm, reduces the spatial-temporal complexity.
机译:概念格子和粗糙集是用于数据分析和处理的强大工具,已成功应用于许多领域。但是,决策信息在许多信息系统中不完整。在本文中,已经提出了不完整概念格的定义,在不完美概念格和粗糙集之间建立了一些关系。本文为基于可辨识矩阵的减少策略效率低下的概率概率,本文给出了一个新的属性概念算法。与基于可辨别矩阵的不完整概念格的属性进行比较,这种减少算法降低了空间时间复杂性。

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