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Association rule mining is a very important research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining. Traditional association rule algorithms adopt an iterative method, which requires very large calculations and a complicated transaction process. FAR (Feature Matrix Based Association Rules) algorithm solves this problem. However, FAR algorithm is not efficient when the value of the minimum support is small or the number of column of the feature matrix is very large. So we proposed a new algorithm (GMA) which based on association graph and matrix pruning to reduce the amount of candidate itemsets. Experimental results show that our algorithm is more efficient for different values of minimum support.