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 $RF_{B} ^{\lambda}$ is a good choice when we deal with an interval-valued information system by means of rough set approach.
翻译：估值的信息系统是单一价值信息系统的通用模式。通过粗略的设定方法,对间隔价值信息系统进行了广泛研究。作者可以从同一个间隔价值信息系统建立许多二进制关系。在本文件中,我们对这些二进制关系进行了一些比较研究,以便提供数字尺度,用以选择处理间隔价值信息系统的适当关系。首先,根据相似度,我们比较了同一间隔价值信息系统产生的最常见的三个二进制关系。第二,我们提出了过境程度和集群程度的概念,并调查了这些概念的特性。最后,我们提供了一些方法,通过过渡程度和集群程度来比较二进制关系。此外,我们利用这些方法分析了从面对面识别数据集引出的最常见的三种关系,并了解到在我们通过粗略设定方法处理间隔价值信息系统时,$RF ⁇ B} ⁇ lambda}是一个良好的选择。
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