Mining closed frequent itemsets in data streams is an important task in stream data mining. Most of the traditional algorithms for mining closed frequent itemsets are Apriori-based which find the frequent itemsets from large amount of candidates, and needs a great deal of time and space. In this paper, an algorithm ItemListFCI for mining closed frequent itemsets in data stream is proposed. The algorithm is based on the sliding window model, and uses a ItemList where the transactions and itemsets are recorded by the column and row vectors respectively. The algorithm first builds the ItemList for the first sliding window. Frequent closed itemsets can be detected by pair-test operations on the binary numbers in the Table. After building the first ItemList, the algorithm updates the ItemList for each sliding window. The frequent closed itemsets in the sliding window can be identified from the ItemList. Algorithms are also proposed to modify ItemList when adding and deleting a transaction. The experimental results on synthetic and real data sets indicate that the proposed algorithm needs less CPU time and memory than other similar methods.