Frequent Itemset Mining Algorithm based on Sampling Method

Frequent itemset mining is an important technique in data mining. This paper employ the sampling method to improve the performance. An in-memory index is presented to store the data information, which is maintained by our proposed algorithm FIMS. We conduct the experiments over two datasets and find that when the sampling rate is reduced, the mining performance will be more efficient.