In this paper, we focus on the problem of mining the approximate frequent itemsets. To improve the performance, we employ a sampling method, in which a heuristic rule is used to dynamically determine the sampling rate. Two parameters are introduced to implement the rule. Also, we maintain the data synopsis in an in-memory data structure named SFIHtree to speed up the runtime. Our proposed algorithm SFIH can be efficiently performed over this tree. We conducted extensive experiments and showed that the mining performance can be improved significantly with a high accuracy when we used reasonable parameters.
Li, H., Zhang, Y., Zhang, N., & Jia, H. (2016). A Heuristic Rule Based Approximate Frequent Itemset Mining Algorithm. In Procedia Computer Science (Vol. 91, pp. 324–333). Elsevier B.V. https://doi.org/10.1016/j.procs.2016.07.087