document: CFI-Stream : mining closed frequent itemsets in data streams Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation . Extensive studies have been done on mining frequent closed itemsets , but they are mainly intended for traditional transaction databases and thus do not take data stream characteristics into consideration . In this paper , we propose a novel approach for mining closed frequent itemsets over data streams . It computes and maintains closed itemsets online and incrementally , and can output the current closed frequent itemsets in real time based on users ' specified thresholds . Experimental results show that our proposed method is both time and space efficient , has good scalability as the number of transactions processed increases and adapts very rapidly to the change in data streams .
doc_bio_tags: O O O O O O O O O O B I I O O O O O O O B I O O O O O O O O O B I I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O