Direct Candidates Generation: A Novel Algorithm for Discovering Complete Share-Frequent Itemsets

The value of the itemset share is one way of evaluating the magnitude of an itemset. From business perspective, itemset share values reflect more the significance of itemsets for mining association rules in a database. The Share-counted FSM (ShFSM) algorithm is one of the best algorithms which can discover all share-frequent itemsets efficiently. However, ShFSM wastes the computation time on the join and the prune steps of candidate generation in each pass, and generates too many useless candidates. Therefore, this study proposes the Direct Candidates Generation (DCG) algorithm to directly generate candidates without the prune and the join steps in each pass. Moreover, the number of candidates generated by DCG is less than that by ShFSM. Experimental results reveal that the proposed method performs significantly better than ShFSM.