Rough set model based on formal concept analysis
Rough set model based on formal concept analysis
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DOI：
10.1016/j.ins.2012.07.052
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发表时间：
2013-02
期刊：
影响因子：
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通讯作者：
Xiangping Kang;Deyu Li;Suge Wang;Kaishe Qu
中科院分区：
文献类型：
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作者：
Xiangping Kang;Deyu Li;Suge Wang;Kaishe Qu
提出了一种基于形式概念分析的粗糙集模型。在该模型中，首先在信息系统中提供代数结构问题的解决方案：从信息系统中推断出格结构，对应的节点称为粗糙概念。探讨了如何在粗糙概念的基础上处理粗糙集理论中常见的问题，如上下近似算子、约简、核等。决策依赖由于其表达性强、易于理解等特点，已成为一种常见的知识表示形式，在实践中得到了广泛的应用。最后，研究了粗糙概念在决策表决策依赖抽取中的应用，从决策表中可以得到一个完备的、无冗余的决策依赖集。算例表明，本文方法的应用是有效的、可行的。研究结果不仅从形式概念分析的角度加深了对粗糙集理论的理解，而且为粗糙集理论与形式概念分析的结合提供了一条新的途径。
This paper proposes a rough set model based on formal concept analysis. In this model, a solution to an algebraic structure problem is first provided in an information system: a lattice structure is inferred from the information system and corresponding nodes are called rough concepts. How to deal with common problems in rough set theory based on rough concepts is then explored, such as upper and lower approximation operators, reducts and cores. Decision dependency has become a common form of knowledge representation owing to its properties of expressiveness and ease of understanding, so it has been widely used in practice. Finally, application of rough concepts to the extraction of decision dependencies from a decision table is studied; a complete and non-redundant set of decision dependencies can be obtained from a decision table. Examples demonstrate that application of the method presented in this paper is valid and practicable. The results not only provide a better understanding of rough set theory from the perspective of formal concept analysis, but also demonstrate a new way of combining rough set theory and formal concept analysis.