Discovery of useful information and valuable knowledge from transactions has attracted many researchers due to increasing use of very large databases and data warehouses. Furthermore most of proposed methods are designed to work on traditional databases in which re-scanning the transactions is allowed. These methods are not useful for mining in data streams (DS) because it is not possible to re-scan the transactions duo to huge and continues data in DS. In this paper, we proposed an effective approach to mining frequent itemsets used for association rule mining in DS named GRM 1 . Unlike other semi-graph methods, our method is based on graph structure and has the ability to maintain and update the graph in one pass of transactions. In this method data storing is optimized by memory usage criteria and mining the rules is done in a linear processing time.Efficiency of our implemented method is compared with other proposed method and the result is presented.