A fast algorithm for mining frequent closed itemsets over stream sliding window

Mining frequent patterns refers to the discovery of the sets of items that frequently appear in a transaction database. Many approaches have been proposed for mining frequent itemsets from a large database, but a large number of frequent itemsets may be discovered. In order to present users fewer but more important patterns, researchers are interested in discovering frequent closed itemsets which is a well-known complete and condensed representation of frequent itemsets. In this paper, we propose an efficient algorithm for discovering frequent closed itemsets over a data stream. The previous approaches need to do a large number of searching operations and computations to maintain the closed itemsets when a transaction is added or deleted. Our approach only performs few intersection operations on the transaction and the closed itemsets related to the transaction without doing any searching operation on the previous closed itemsets. The experimental results show that our approach significantly outperforms the previous approaches.