摘要：Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper considers a simple but effective algorithm for mining maximal frequent itemsets over a stream landmark. We design a compact data structure named FP-FOREST to improve an state-of-the-art algorithm INSTANT; thus, itemsets can be compressed and the support counting can be effective performed. Our experimental results show our algorithm achieves a better performance in memory cost and running time cost.
