粗糙集理论中属性值粗化和细化过程中近似的动态维护方法研究

Hongmei Chen, Tianrui Li, Weibin Liu, Weili Zou
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引用次数: 9

摘要

在粗糙集理论(RST)中,当信息系统随时间变化时,概念的上近似和下近似会动态变化。如何在原始近似信息的基础上更新近似是一个重要的问题,因为它可以提高知识发现的效率。本文主要研究属性值粗化或精化时的动态更新逼近方法。介绍了信息系统中属性值粗化和细化的定义。给出了属性值粗化和细化时上下近似的动态维持性质。最后,分析了多粒度属性值粗化或细化的原理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the approach of dynamically maintenance of approximations in rough set theory while attribute values coarsening and refining
In rough set theory (RST), upper and lower approximations of a concept will change dynamically while the information system varies over time. How to update approximations based on the original approximations' information is an important problem since it may improve the efficiency of knowledge discovery. This paper focuses on the approach for dynamically updating approximations when attribute values coarsening or refining. The definitions of attribute values coarsening and refining in information systems are introduced. The properties for dynamic maintenance of upper and lower approximations while attribute values coarsen and refine are presented. Finally, the principle of coarsening or refining of the multi-granularity attribute values is analyzed.
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