Abstract: To overcome the difficulty of updating frequent item sets in the dynamic database, this study proposes a new algorithm for efficiently mining association rules in dynamic updated databases. The algorithm constructs the corresponding vector subspace according to the number of nonempty subsets in the item sets which is based on the concept of the Apriori algorithm that the maximal frequent item sets are definitely the subsets of database’s item set. After the construction of the vector subspace, the dynamic tuples additions and deletions of the database, as well as the updated solutions to the frequent item sets when the minimum support is changed, are determined efficiently by the vector inner computing. Studies show that the algorithm is not only simple in that it needs only to scan the database once, but also capable of processing super database.