A new algorithm for mining frequent closed itemsets from data streams

Mining frequent closed itemsets from data streams has been studied extensively. Algorithm MOMENT and its modified algorithm A-MOMENT were regarded as typical methods. Both of them depend on a data structure named CET. This paper designs a new data structure FULL-CET and proposes a new mining frequent closed itemsets algorithm MFCIDS based on landmark window. Differing entirely from traditional methods which find new frequent itemsets through union operations on existed frequent itemsets, MFCIDS records the support of each closed frequent itemset to maintain all frequent closed itemsets through intersection operations on nodes appearing actually in transactions. Experimental results show that MFCIDS performs better than MOMENT and its modified algorithm A-MOMENT on efficiency and scalability.