MINING RECENT FREQUENT ITEMSETS IN SLIDING WINDOWS OVER DATA STREAMS
Congying Han, Lijun Xu, Guoping He · 2008
This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an effi- cient one-pass algorithm in sliding windows over data streams with an error bound guarantee. This algorithm does not need to refer to obsolete transactions when