An Efficient Algorithm for Mining Closed Frequent Itemsets in Data Streams

Mining closed frequent itemsets in the sliding window is one of important topics of data streams mining. In this paper, we propose a novel algorithm, FPCFI-DS, which mines closed frequent itemsets in the sliding window of data streams efficiently, and maintains the precise closed frequent itemsets in the current window at any time. The algorithm uses a single-pass lexicographical-order FP-Tree-based algorithm with mixed item ordering policy to mine the closed frequent itemsets in the first window, and introduces a novel updating approach to process the sliding of window. The experimental results show that FPCFI-DS performs better than the state-of-the-art algorithm Moment in terms of both the time and space efficiencies, especially for dense dataset or low minimum support.