Quick attribute reduction algorithm based on improved frequent pattern tree

The attribute reduction algorithms designed by the method of discernibility 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 calculating 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.