Abstract: Mining frequent itemsets over data streams is an emergent research topic in recent years. In data streams, new data are continuously coming as time advances. It is costly even impossible to store all streaming data received so far due to the memory constraint. It is assumed that the stream can only be scanned once and hence if an item is passed, it cannot be revisited, unless it is stored in main memory. Storing large parts of the stream, however, is not possible because the amount of data passing by is typically huge. The previous approaches, accept only one minimum support and using fixed window length. In reality, the minimum support is not a fixed value for the entire stream of transactions. In this paper we propose a new method, which used multiple segments for handling different size of windows over data streams. By storing these segments in a data structure, the usage of memory can be optimized.