摘要：Mining frequent itemsets from data stream is an important task in stream data mining. This paper presents an algorithm Stream_FCI for mining the frequent closed itemsets from data streams in the model of sliding window. The algorithm detects the frequent closed itemsets in each sliding window using a DFP-tree with a head table. In processing each new transaction, the algorithm changes the head table and modifies the DFP-tree according to the changed items in the head table. The algorithm also adopts a table to store the frequent closed itemsets so as to avoid the time-consuming operations of searching in the whole DFP-tree for adding or deleting transactions. Our experimental results show that our algorithm is more efficient and has lower time and memory complexity than the similar algorithms Moment and FPCFI-DS.
关键词：Stream data;mining closed frequent data itemsets;sliding window
