MFIS - Mining frequent itemsets on data streams

We propose an efficient approach to mine frequent Itemsets on data streams. It is a memory efficient and accurate one-pass algorithm that can deal with batch updates. The proposed algorithm performers well by dividing all frequent itemsets into frequent equivalence classes and pruning all redundant itemsets except for those that represent GLB (Greatest Lower Bound) and LUB (Least Upper Bound) of the frequent equivalence classes. The number of GLB and LUB is much less than the number of frequent itemsets. The experimental evaluation on synthetic and real datasets shows that the algorithm is very accurate and requires significantly lower memory than other well-known one-pass algorithms.