Parallel Binary Approach for Frequent Itemsets Mining

Abstract

Abstract-The technique of association rules discovering is one of the most known and the most explored techniques of data mining. This technique has two main phases: the first is to extract all the frequent itemsets and the second is to generate association rules from these frequent itemsets. The first phase is the most expensive given the large number of accesses to transactions database and the large number of candidate itemsets. As databases are generally very large, a solution to avoid the repetitive and costly accesses is to represent them by compact structures. In this paper, we propose a parallel binary approach for frequent itemsets extracting, to deal with the great number of candidates and to take advantage of multicore architectures. This approach is implemented using a compact data structure based on signatures tree for the representation of the database to access it only once. Index Terms-Data structure, frequent itemsets, open MP, parallelism.