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Mining association rules with multiple minimum supports is an important research aspect of data mining. In this paper we propose a database partition method to mine the frequent item sets, and use MIS-tree to store the crucial information about frequent patterns. We use the CFP-growth algorithm to mine local frequent patterns and insert them into the global frequent pattern. The experiment on OASN shows that the method is effective to predict the optical warning level.