A new sliding window based algorithm for frequent closed itemset mining over data streams

Data stream mining is an important problem in the context of data mining and knowledge discovery. Mining frequent closed itemsets within sliding window instead of complete set of frequent itemset is very interesting since it need a limited amount of memory and processing power. In this paper, we introduce an effective algorithm for closed frequent itemset mining which operates in sliding window model. This algorithm uses a novel data structure for storing transactions of the window and corresponding closed itemsets. Moreover, the supports of itemsets are computed efficiently. Experimental evaluations show that the algorithm is superior to a recently proposed algorithm in terms of runtime and memory usage.