Discretization based on rough sets is used to divide the space formed by continuous attribute values with as few breakpoint sets as possible, while maintaining the original indistinguishable relationship of the decision system, so as to accurately classify and identify related information. In this study, a discretization algorithm for incomplete economic information in rough set based on big data is proposed. First, the algorithm for filling-in incomplete economic information based on deep learning is used to supplement the incomplete economic information. Then, based on breakpoint discrimination, the algorithm for discretization in the rough set is used to implement the discretization based on rough set for supplementary economic information. The performance of this algorithm was tested using multiple sets of data and compared with other algorithms. Experimental results show that this algorithm is effective for discretization based on a rough set of incomplete economic information. When the number of incomplete economic information rough candidate breakpoints increases, it still has a higher computational efficiency and can effectively improve the integrity of incomplete economic information, and finally the application performance is superior.

中文翻译：

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基于大数据的粗糙集中不完全经济信息离散化算法

使用基于粗糙集的离散化，用尽可能少的断点集划分连续属性值形成的空间，同时保持决策系统原有的不可区分关系，从而对相关信息进行准确分类识别。本研究提出了一种基于大数据的粗糙集中不完全经济信息离散化算法。首先，利用基于深度学习的不完整经济信息填充算法对不完整经济信息进行补充。然后，基于断点判别，利用粗糙集离散化算法实现基于粗糙集的离散化，以补充经济信息。使用多组数据测试了该算法的性能，并与其他算法进行了比较。实验结果表明，该算法对于基于不完整经济信息的粗糙集进行离散化是有效的。当不完整经济信息粗略候选断点数量增加时，仍然具有较高的计算效率，可以有效提高不完整经济信息的完整性，最终应用性能优越。