不同不完全软集中缺失数据的检索

J. Srivastava, Sudhir Maddheshiya
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引用次数: 1

摘要

不完全信息的处理一直是软集理论中的一个主要问题。本文提出了一种处理不完全软集、不完全模糊软集和不完全直觉模糊软集的方法。为此,我们讨论了两个对象(参数)之间的距离的概念,它将用于计算它们之间的相互依赖程度。这种方法将使用已知数据的全部信息以及它们之间的关系。数据填充将不完整的软集转换为完整的软集,使软集不仅适用于决策,而且适用于其他领域。
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Retrieving the Missing Data From Different Incomplete Soft Sets
Dealing an incomplete information has been a major issue in the theory of soft sets. In this paper, we have presented an approach to deal with incomplete soft set, incomplete fuzzy soft set and incomplete intuitionistic fuzzy soft set. For this purpose we have discussed about the notion of distance between two objects (parameters) which will be used to compute the degree of interdependence between them. This approach will use the full information of known data and the relationships between them. Data filling converts an incomplete soft set into complete one which makes the soft sets applicable not only to decision making but also to other fields.
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