Mining high dimensional association rules by generating large frequent k-dimension set

Abstract

Association rule mining aims at generating association rules between sets of items in a database. Now a day, due to huge accumulation in the database technology, the data are representing in the high dimensional data space. However, it is becoming very tedious to generate association rules from high dimensional data, because it contains different dimensions or attributes in the large data bases. In this paper, a method for generating association rules from large high dimensional data is proposed . It constitutes three steps, 1) pre-processing and generalizing the data base; 2) it generates large frequent k-dimension set using user supplied support value which is more feasible than the traditional approach; and 3) generating strong association rules using confidence. It can be seen from experiments that the mining algorithm is elegant and efficient, which can obtain more rapid computing speed and sententious rules at the same time It was ascertained that the proposed method is proved to be better in support of generating association rules.