A Fast Approach for Up-Scaling Frequent Itemsets

With the rapid growth of data scale and diversification of demand, people have an urgent desire to extract useful frequent itemset from datasets of different scales. It is no doubt that the traditional method can solve the problem. However, the relationships among datasets of different scales are not fully utilized. A fast approach proposed in this paper is as follows: the frequent itemsets on the large-scale data are directly inferred based on the frequent itemsets that are belonged small-scale datasets, instead of mined from the large-scale dataset again on condition that the frequent itemsets on the small-scale datasets have been mined. We conduct extensive experiments on one synthetic data and four UCI data sets. The experimental results show that our algorithm is significantly faster and consumes less memory than these leading algorithms.