A Parallel Algorithm for Frequent Itemset Mining

摘要：

Frequent itemsets mining plays an essential role in data mining. In this paper, a new algorithm PFP-growth (Parallel FP-growth), which is based on the improved FP-growth, is proposed for parallel frequent itemset mining.The new algorithm distributes the task fairly among the parallel processors. We devise partitioning strategies at different stages of the mining process to achieve balance between processors and adopt some data structure to reduce the information transportation between processors. The experiments on national high performance parallel computer show that the PFP-growth is an efficient parallel algorithm for mining frequent itemset.

关键词： Parallel Frequent Itemset Data mining PFP-growth

作者: Li Li Donghai Zhai Fan Jin

作者单位: School of Computer and Communications Engineering Southwest Jiaotong University, Chengdu, 610031, P.R.CHINA

会议类型: 国际会议

会议名称: Proceedings of The Fourth International Conference on Parallel and Distribyted Computing,Applications and Technologies(第四届并行与分布式计算应用与技术国际会议)

会议地点: 成都

会议语种:英文

页码: 868-871

在线出版日期: 2003-08-27（万方平台首次上网日期，不代表论文的发表时间）