An efficient algorithm for frequent itemset mining on data streams

Abstract

In order to mining frequent itemsets on data stream efficiently, a new approach was proposed in this paper. The memory efficient and accurate one-pass algorithm divides all the frequent itemsets into frequent equivalence classes and prune all the redundant itemsets except for those represent the GLB(Greatest Lower Bound) and LUB(Least Upper Bound) of the frequent equivalence class and the number of GLB and LUB is much less than that of frequent itemsets. In order to maintain these equivalence classes, A compact data structure, the frequent itemset enumerate tree (FIET) was proposed in the paper. The detailed experimental evaluation on synthetic and real datasets shows that the algorithm is very accurate in practice and requires significantly lower memory than Jin and Agrawal's one pass algorithm. © Springer-Verlag Berlin Heidelberg 2006.