摘要
现有的约简方法主要是采用基于区分矩阵的基本算法及启发式算法.前者只适用于极小规模数据,后者则不能保证完备性.文中在研究粗糙集等价类与概念格外延之间的对应关系基础上,重点研究基于概念格模型的粗集约简的相关问题的求解.在此基础上提出基于概念格模型的粗集完备约简算法.实验结果表明该算法提高约简的时空性能.
The exitsing reduction methods mainly use basic algorithm or heuristic algorithm based on discernibility matrix. However, the former can only be applied to the small dataset and the latter can not guarantee completeness： On the basis of studying the mapping relation between equivalence class and extension, the relevant solution of rough set based on concept lattice is mainly studied. Moreover, a complete reduction algorithm is proposed based on concept lattice and the test results show that the proposed algorithm enhances the performance of time and space.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第4期624-629,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60573174)
安徽省自然科学基金(No.050420207)资助项目
关键词
粗糙集
概念格
核
约简
Rough Set, Concept Lattice, Core, Reduction
