An Effective Hybrid Algorithm for Fast Mining Frequent Itemsets

摘要

PPC-Tree and N-list have been proven to be very efficient and been used in mining frequent itemsets widely. The main problem of the novel structures is that the way of First Constructing Last Encoding method is adopted in the tree-building phase. This causes excessive time consumption to mine frequent itemsets. In this paper, we propose SFO-Set based on SFO-Tree, a more efficient data structure, to mine frequent itemsets. SFO-Tree construction employs the Constructing and Encoding method in comparison with PPC-Tree. Based on SFO-Sets and Subsume Index, FI_SS algorithm, an efficient hybrid method for mining frequent itemsets, is proposed. For evaluating the performance of FI_SS, we conduct lots of experiments on a variety of public datasets. Experimental results show that FI_SS algorithm has advantages in running time.