A Novel Approach for Mining High Dimensional Association Rules Using 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 and incredible growth in high dimensional dataset, conventional data base methods are inadequate in extracting useful information. Such large high dimensional data gives rise to a number of new computational challenges not only the increased in number of data objects but also in the increased in number of features/attributes. 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. To improve the high dimensional data mining task, it must be preprocessed efficiently and accurately. In this paper, an Apriori based method for generating association rules from large high dimensional data is proposed. It constitutes 1) Preprocessing and generalizing the data base dimensions; 2) generating high dimensional strong association rules using support and 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.