A Simple But Effective Stream Maximal Frequent Itemset Mining Algorithm

Maximal frequent item sets are one of several condensed representations of frequent item sets, which store most of the information contained in frequent item sets using less space, thus being more suitable for stream mining. This paper focuses on mining maximal frequent item sets approximately over a stream landmark model. We separate the continuously arriving transactions into sections, and the mining results are indexed by an extended direct update tree, thus, a simple but effective algorithm named SMIS is proposed. In our algorithm, we employ the Chern off Bound to perform the maximal frequent item set mining in a false negative manner, which can reduce the memory cost, as well guarantee our algorithm conducting with an incremental fashion. Our experimental results on two synthetic datasets and two real world datasets show that SMIS achieves much reduced memory cost in comparison with the state-of-the-art algorithm with a 100 percent precision.