论文标题
基于横向程度和群集度的二元关系的比较研究
Comparison research on binary relations based on transitive degrees and cluster degrees
论文作者
论文摘要
间隔值信息系统是单值信息系统的广义模型。通过粗略的设定方法，已经对间隔值的信息系统进行了广泛的研究。作者可以通过相同的间隔值信息系统建立许多二进制关系。在本文中，我们对比较这些二进制关系进行了一些研究，以便提供数值量表，以选择适当的关系来处理间隔值的信息系统。首先，根据相似程度，我们比较了从相同的间隔值信息系统引起的最常见的三个二进制关系。其次，我们提出了传递程度和聚类程度的概念，并研究其特性。最后，我们提供了一些方法来通过横向程度和集群程度比较二进制关系。此外，我们使用这些方法来分析面部识别数据集引起的最常见的三个关系，并获得$ rf_ {b} ^λ$是一个不错的选择，当我们通过粗糙设置方法处理间隔值的信息系统时。
Interval-valued information systems are generalized models of single-valued information systems. By rough set approach, interval-valued information systems have been extensively studied. Authors could establish many binary relations from the same interval-valued information system. In this paper, we do some researches on comparing these binary relations so as to provide numerical scales for choosing suitable relations in dealing with interval-valued information systems. Firstly, based on similarity degrees, we compare the most common three binary relations induced from the same interval-valued information system. Secondly, we propose the concepts of transitive degree and cluster degree, and investigate their properties. Finally, we provide some methods to compare binary relations by means of the transitive degree and the cluster degree. Furthermore, we use these methods to analyze the most common three relations induced from Face Recognition Dataset, and obtain that $RF_{B} ^λ$ is a good choice when we deal with an interval-valued information system by means of rough set approach.
