Abstract: Mining closed frequent itemsets over stream data is an important research issue of mining association rules in data mining. In this paper, we propose an efficient closed frequent itemsets mining algorithm in stream data, CFMoment, to maintain the set of closed frequent itemsets in data streams with a sliding window. The new algorithm can be applied to many stream data processing applications with high real-time requirements. It proposes to reduce the time and memory requirements in sliding windows by using the effective bit-sequence representation of items, which further improves the efficiency of closed frequent itemsets in stream data mining and effectively reduces the memory requirements in running process. Experiments show that the proposed algorithm not only attains highly accurate mining results, but also runs significantly faster and consumes less memory than the existing algorithm Moment.

Key words: data streams, data mining, sliding window, closed frequent itemsets