相关实验视频
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Spatial Separation of Molecular Conformers and Clusters
Published on: January 9, 2014
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改进了基于变量J-封闭社区的粗略近似.
1School of Mathematical Sciences, Anhui University, 111 Jiulong Road, Hefei, 230601 Anhui People's Republic of China.
概括
本研究介绍了变量j-containment社区,这是粗略集合理论的灵活方法,可以确保反射性和可调整的细粒度,以改善不完整信息系统中的属性减少.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 集合理论 集合理论
背景情况:
- 邻里系统中的经典概括粗略集模型提供了广泛的框架,但可能缺乏反射性.
- 现有的社区类型 (粘附性,封闭性,j-社区) 在反射性和颗粒度上有局限性 (过于细或过于粗).
研究的目的:
- 提出一个新的社区建设:可变的j-封闭社区 (VjCNs).
- 为了确保邻居空间的反射性和灵活的颗粒度,用于粗的应用.
- 在不完整的信息系统中加强属性减少.
主要方法:
- 设计的可变j-封闭社区 (VjCNs) 满足反射性和可调颗粒度.
- 在VjCN空间内概括了三种类型的粗略近似.
- 分析了基于VjCNs的拓结构,并与现有方法进行了比较.
主要成果:
- VjCNs通过参数"j"提供灵活的颗粒度调整.
- 拟议的粗略设置模型表明,在不完整数据的属性减少方面存在优势.
- 这种方法确保了反射性,解决了以前社区类型的局限性.
结论:
- 变量j-包含社区为粗略的集合理论提供了一个灵活和反射的框架.
- VjCN模型增强了属性减少,特别是在不完整的信息系统中.
- 与现有的社区系统相比,这种新的方法提供了更具适应性的细粒度.
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