An Efficient Algorithm for Mining Closed Frequent Itemsets in Data Streams
Fujiang Ao, Jing Du, Yuejin Yan, Baohong Liu, Kedi Huang · 2008
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.