摘要
粗糙集理论是一种新型的处理模糊和不确定知识的数学工具。目前已在人工智能、知识与数据发现、模式识别与分类、故障检测等方面得到了广泛应用。首先描述了粗糙集的基本算法及其复杂度 ,包括等价关系 ,上下近似及各种约简算法 ;接着对粗糙集扩展理论 ,如可变精度模型 ,相似模型等进行了讨论 ,然后对粗糙集在数据挖掘、大数据集、粗糙逻辑、多方法融合等领域中的应用进展情况进行了论述 。
Rough set theory, a new mathematical tool dealing with vagueness and uncertainty, was introduced by Pawlak in 1982. It has been widely used in the area of AI, data mining, pattern recognition, fault diagnositics, etc. This paper describes the basic algorithms for rough set theory, including equivalent relation, upper/lower approximation and reduction. Then several extensions of rough set theory are discussed such as VPRS, similarity based model, and applications of rough set theory in areas like data mining, rough logic, etc. Further research directions are then discussed.
出处
《清华大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2001年第1期64-68,共5页
Journal of Tsinghua University(Science and Technology)
基金
国家自然科学基金资助项目!(79990 5 80 )
国家"九七三"基础研究项目!(G19980 30 414 )
