A novel method of mining frequent item sets

The aim of mining association rules is to discover the association relationship among the item sets from mass data. In some practical applications, its role is mainly to assist decision-maker. The paper proposes a novel association rule algorithm of mining frequent item sets, which introduces a new data structure and adopts compressed storage tree to improve the run performance of this algorithm. At last, the experiment indicates that the algorithm proposed in this paper has much more advantages in load balance and run time compared with most existing algorithms.