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Representing attribute reduction and concepts in concept lattice using graphs

机译:代表使用图形概念格的属性减少和概念

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Concept lattice is an area of research which is based on a set-theoretical model for concepts and conceptual hierarchies. It is better for the studying of concept lattice to minimize the input data before revealing the construction of a concept lattice. Actually, this duty can be done by attribute reduction for a context. Graph is useful in data analysis since it gives us a visual trend on the behavior of our data points and allows us to test some laws in data analysis. This paper is a preliminary attempt to study how directed graph can be used on attribute reduction and conceptual construction in concept lattices. We investigate all the reducible attributes and concepts in a context with the aid of graph theory. For a context, we define a relevant graph on the set of attributes and, further, define a pre-weighted relevant graph. Afterward, using relevant graphs and pre-weighted relevant graphs with the method of deleting vertices in a directed graph, we find all the reducible attributes in a context. After that, we seek out all of concepts and the concept lattice for a given context. All these results may be not only used to improve the visibility and readability of attribute reduction in a context and the construction of concept lattice, but also broaden the applied range of directed graph such as in the field of attribute reduction.
机译:概念格子是一种研究领域,基于概念和概念层次结构的设定理论模型。在揭示概念晶格的构建之前,更好地研究概念格子以最小化输入数据。实际上,可以通过减少上下文来完成这种职责。图表在数据分析中有用,因为它为我们提供了对我们数据点的行为的视觉趋势,并允许我们在数据分析中测试一些法律。本文是研究如何在概念格子的属性减少和概念构建上使用指示图的初步尝试。我们在图表理论的帮助下调查了所有还原的属性和概念。有关上下文,我们在属性集合上定义相关图,并进一步定义预加权相关图。之后,使用相关图和预加权相关图,其中包含删除顶点的方法,我们在上下文中找到所有可还原属性。之后,我们寻找给定环境的所有概念和概念格子。所有这些结果不仅可以用于改善概念格的上下文和构建的属性减少的可见性和可读性,而且还扩大了所施加的指导图的范围,例如在属性降低领域。

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