An Improved Apriori Algorithm Based On the Boolean Matrix and Hadoop

AbstractThe association rule mining plays an important part in the data mining. Association rule mining aims to find rules in the transaction database with the minimum support and minimum confidence which are the user given. In order to find all the frequent item sets from the transaction database efficiently and quickly, an improved Apriori algorithm of mining the association rules in this paper is put forward to solve the bottleneck problems of the traditional Apriori algorithm. First the Boolean matrix array is used to replace the transaction database. Then the “AND” operation and random access characteristics of array are used. Next the mining algorithm is carried out on the Hadoop Platform. According to the number of the Data Nodes of the Hadoop, the matrix is divided into several parts. Each part is operated separately on one Data Node. It can improve the efficiency of the algorithm