A FAST ALGORITHM FOR CONSTRUCTING FP_TREE

摘要：

Recently, most of the studies on mining frequent patterns focus on improving the efficiency of frequent itemtsets generations, but the I/O cost of database scanning has been a bottle-neck problem in data mining.Many algorithms proposed recently are based on A priori and FP_tree, and the FP_growth algorithm based on FPtree is more efficient than Apriori because the candidates are not generated.But the construction of FP_tree may spend much time.Therefore, the goal of our research is to propose a fast algorithm.In this paper.Level FP_tree that is constructed level by level (abbreviate LFP_tree) is proposed.The algorithm contains two main parts.The first is to scan the database only once for generating equivalence classes of each item.The second is to delete the non-frequent items and rewrite the equivalence classes of the frequent items, and then construct the LFP_tree.Experimental results have proved that LFP_tree is more efficient and scalable than FP_tree.

关键词： Frequent pattern FP_growth FP_tree LFP_tree Equivalence class

作者: JIAO-MIN LIU SHENG GUO ZHEN-ZHOU WANG

作者单位: College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazh College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazh College of Electrical Engineering, North China Electric Power University, Baoding 071003, Hebei, Chi

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2390-2394

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）