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用于粗糙集约简的并行算法

孙涛, 董立岩, 李军, 张羽翔   

  1. (吉林大学 计算机科学与技术学院, 长春 130012)
  • 收稿日期:2005-04-11 修回日期:1900-01-01 出版日期:2006-03-26 发布日期:2006-03-26
  • 通讯作者: 董立岩

Parallel Algorithm for Rough Set Reduction

SUN Tao, DONG Li-yan, LI Jun, ZHANG Yu-xiang   

  1. (College of Computer Science and Technology, Jilin University, Changchun 130012, China)
  • Received:2005-04-11 Revised:1900-01-01 Online:2006-03-26 Published:2006-03-26
  • Contact: DONG Li-yan

摘要: 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.

关键词: 数据挖掘, 粗糙集, 属性约简, 并行算法

Abstract: By the research of data mining rough set reduct, the author propounded a data dividing thought based on the discernibility ability of i ndex. First the information system was divided into many small tables, then the final reduction was resolved from the small table reduction. This thought has pre digested 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.

Key words: data mining, rough set, attribute reduction, parallel algorithm

中图分类号: 

  • TP18