Quick Attribute Reduction Algorithm Based on Improved Frequent Pattern Tree
The attribute reduction algorithms designed by the method of disccrnibility matrix have lots of repeat and unnecessary elements in the discernibility matrix, which not only cost a mass of memory space, but also waste plenty of computing time for calcluating attribute reduction, in order to improve the efficiency of such attribute reduction algorithm, by considering the idea of FP tree, a novel data structure IFP(improved frequent pattern) tree is proposed, which can get rid of the repeat elements and unnecessary elements in the discernibility matrix completely. In this way, it can not only reduce a great deal of memory space, but also enhance the efficiency of attribute reduction algorithm greatly. Then, a new quick and efficient attribute reduction algorithm is designed based on IFP_tree, Finally, an example is used to illustrate the validity of the new algorithm.
rough set discernibility matriz attribute reduction IFP(improved frequent pattern) tree
Zhangyan Xu Liyu Huang Wenbin Qian Bingru Yang
College of Computer Science and Information Engineering,Guangxi Normal University,Guilin China,54100 School of information engineering,University of Science and Technology Beijing,Beijing100083,China
国际会议
上海
英文
406-410
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）