Abstract :
ships among sets of items is an important problem in data mining. Finding frequent itemsets is computationally the most expensive step in a association rules discovery algorithms. Therefore, it has grasped significant research focus. Most of the previous studies adopt Apriori-like algorithms, whom iteratively generate candidate itemsets and check their frequencies in the database. These approaches suffer from serious costs of repeated passes over the database. To address this problem, we propose a new parallel method, called PARALLELTREESUPBDD-MINE, for reducing cost time to find frequent itemset discovery algorithms. The idea of PRALLELTREESUPBDD-MINE consists in using a Binary De- cision Diagram (BDD) and a prefix tree for representing both database and frequent itemsets. The proposed method requires only one scan over the source database to create the associated tree and BDD and to check discovered itemset supports. The originality of our work stands on the fact that the proposed algorithm extracts in a parallel manner the frequent itemsets directly from the TREESUPBDD. We have tested our algorithm using different benchmark datasets and we have obtained good results. Keywords: Data mining, Association rules, Frequent itemsets, Binary decision diagram, Parallel data mining.
