A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. However, most of mining algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a sliding window method of finding recently frequent itemsets over an online data stream. The size of a window defines a desired life-time of the information of a transaction in a data stream.