An Algorithm for Mining Frequent Closed Itemsets in Data Stream

Mining frequent itemsets from data streams by the model of sliding window has been extensively studied. This paper presents an algorithm AFPCFI-DS for mining the frequent itemsets from data streams. The algorithm detects the frequent items using a FP-tree in each sliding window. In processing each new window the algorithm first changes the head table and then modifies the FP-tree according to the changed items in the head table. The algorithm also adopts local updating strategy to avoid the time-consuming operations of searching in the whole tree to add or delete transactions. Our experimental results show that the algorithm is more efficient and has lower time and memory complexity than the algorithms Moment and FPCFI-DS.