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New Parallel Algorithm for Mining Frequent Item Sets Based on FP_Growth(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2016年04期
Page:
0-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
New Parallel Algorithm for Mining Frequent Item Sets Based on FP_Growth
Author(s):
Sun Hongyan; Ji Genlin
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
frequent item sets; association rule; FP_Growth; Hadoop; Map/Reduce
PACS:
TP311.11
DOI:
10.3969/j.issn.1001-4616.2016.04.005
Abstract:
Mining of frequent item sets is used to find the association rules between items. In order to get frequent item sets of big data efficiently,this paper proposes a new parallel algorithm for mining frequent item sets based on FP_Growth,named NPFP_Growth(New Parallel algorithm based on FP_Growth). The storage structure of local frequent pattern tree is improved and created in each node based on parallel computing model Map/Reduce and distributed storage system HDFS,and then longest global frequent item sets are mined in each branch of the tree. Finally,Support for item sets which does not meet global minimum support is computed and then sent to corresponding computing node to count. Parallel mining algorithm NPFP_Growth is implemented. The experimental results show that the algorithm have high computing efficiency and good scalability.

References:

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Last Update: 2016-12-31