节点文献
多粒度粗糙集的双层绝对约简
Double-Level Absolute Reduction for Multi-granulation Rough Sets
【摘要】 多粒度粗糙集本质上是异构的,但是目前尚未运用于异构数据处理.从绝对约简的角度出发,提出多粒度粗糙集的双层绝对约简——多粒度绝对约简和多粒度绝对粒度约简.分析多粒度双层绝对约简的性质,特别是从异构数据约简的角度探究多粒度双层绝对约简的特性,提出多粒度双层绝对约简算法.理论分析和实例表明多粒度双层绝对约简算法的可行性.
【Abstract】 Multi-granulation rough set is a rough set model for heterogenous data in essence. However,it is still not employed to deal with heterogenous data. From the viewpoints of absolute attribute reduction,double-level absolute reduction for multi-granulation rough sets is proposed,including multi-granulation absolute recducts and multi-granulation absolute granulation reducts, and properties of double-level absolute reduction are analyzed from the perspective of heterogenous data. The algorithms for double-level absolute reduction are presented. Theoretical analysis and example show the validation of multi-granulation absolute reducts, multi-granulation absolute granulation reducts and double-level absolute reducts.
【Key words】 Rough Set; Multi-granulation Rough Set; Heterogenous Data; Absolute Reduction; Double-Level Absolute Reduction;
- 【文献出处】 模式识别与人工智能 ,Pattern Recognition and Artificial Intelligence , 编辑部邮箱 ,2016年11期
- 【分类号】TP18
- 【被引频次】4
- 【下载频次】123