An Improved FP-Growth Algorithm Based on Hadoop Platform
PAN Junhui
WANG Hui
ZHANG Qiang
WANG Haochang
Abstract：FP-Growth algorithm is an optimization algorithm for mining association rules,but it has some disadvantages such as large memory consumption and low computational efficiency when mining massive data in a single machine.In this paper,an im-proved FP-Growth algorithm is proposed by introducing the merged pruning strategy,and implemented on Hadoop platform.At the same time,in order to improve the execution efficiency,the dynamic grouping strategy is adopted to realize the load balancing.The experimental results show that the modified FP-growth algorithm based on Hadoop platform has certain advantages in processing massive data.
Keywords：FP-Growthassociation rulesmerged pruningdynamic groupingHadoop
Publication Date：2024-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 3481-3484,3546 )