An algorithm has been proposed for mining frequent maximal itemsets from data cube. Discovering frequent itemsets has been a key process in association rule mining. One of the major drawbacks of traditional algorithms is that lot of time is taken to find candidate itemsets. Proposed algorithm discovers frequent itemsets using aggregation function and directed graph. It uses directed graph for candidate itemsets generation and aggregation for dimension reduction. Experimental results show that the proposed algorithm can quickly discover maximal frequent itemsets and effectively mine potential association rules.