摘要
Pretreatment of mass and high dimensional data for users plays an important role for data mining in grid environment. To solve optimal reduction effectively, a distributed reduction algorithm on grid service is present. It combines grid services with a novel reduction algorithm on gene expression programming （GEP） （RA-GEP）. Simulation experiments show that for mass or high dimensional data sets, the proposed algorithm has advantages in terms of speed and quality in contrast with traditional attribution reduction algorithms on intelligence computing.
Pretreatment of mass and high dimensional data for users plays an important role for data mining in grid environment. To solve optimal reduction effectively, a distributed reduction algorithm on grid service is present. It combines grid services with a novel reduction algorithm on gene expression programming （GEP） （RA-GEP）. Simulation experiments show that for mass or high dimensional data sets, the proposed algorithm has advantages in terms of speed and quality in contrast with traditional attribution reduction algorithms on intelligence computing.
基金
supported by the National Natural Science Foundation of China (60973139,60773041)
the Natural Science Foundation of Jiangsu Province (BK2008451)
the Innovation Project for University of Jiangsu Province (CX09B_153Z,CX08B-086Z)
