用于粗糙集约简的并行算法

通过对数据挖掘粗糙集约简算法的研究, 提出一种基于区分能力指数的信息系统数据划分思想. 先将系统按属性区分能力分成若干子表, 再由子表的约简求原系统的约简, 这种思想较好地简化了布尔函数的化简过程. 根据该思想设计了一个属性约简并行算法, 并利用Petri网模拟工具CPN Tools对算法的负载平衡进行了分析, 通过实验与相应的串行算法在时间上进行了对比, 实验结果显示, 该算法对于对象较多的大规模系统具有较高的效率.

Parallel Algorithm for Rough Set Reduction

By the research of data mining rough set reduct,the author propounded a data dividing thought based on the discernibility ability of index.First the information system was divided into many small tables,then the final reduction was resolved from the small table reduction.This thought has predigested the reduction of the bool function reduction.And based on this thought,a parallel algorithm of attribute reduction was(designed.) By using the simulate tool CPN Tools of Petri Nets,the load balance of the algorithm was analyzed.On the basis of the experimental results compared with the corresponding serial algorithm,it is shown that the(parallel) algorithm is more efficient for the large scale system with excessive objects.