Algorithm Research & Explore
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689-695

Research on parallel frequent itemset mining algorithm based on MapReduce

Liu Weiming
Zhang Chi
Mao Yimin
School of Information Engineering Jiangxi University of Science & Technology, Ganzhou Jiangxi 341099, China

Abstract

Aiming at the problem of excessive time, space complexity and unbalanced load for each node based on the parallel frequent itemset mining algorithm MRPrePost, this paper proposed an optimization parallel frequent itemset mining algorithm based on MapReduce, named PFIMD. Firstly, this algorithm adopted a data structure called DiffNodeset, which effectively avoided the defect that the N-list cardinality got very large in the MRPrePost algorithm. Secondly, in order to reduce the time complexity of this algorithm, it designed the T-wcs to avoid the invalid calculation in the procession of two DiffNodesets connection. Finally, considering the impact of cluster load on the efficiency of parallel algorithm, it proposed the LBSBDG, which decreased the size of PPC-Tree on each computing node and reduced the amount of time required to traverse the PPC-Tree by evenly grouping each item in the F-list. The experimental results show that the modified algorithm has better performance on mining frequent itemset in a big data environment.

Foundation Support

国家自然科学基金资助项目(41562019)
国家重点研发计划资助项目(2018YFC1504705)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.02.0039
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 3
Section: Algorithm Research & Explore
Pages: 689-695
Serial Number: 1001-3695(2021)03-009-0689-07

Publish History

[2021-03-05] Printed Article

Cite This Article

刘卫明, 张弛, 毛伊敏. 基于MapReduce的并行频繁项集挖掘算法研究 [J]. 计算机应用研究, 2021, 38 (3): 689-695. (Liu Weiming, Zhang Chi, Mao Yimin. Research on parallel frequent itemset mining algorithm based on MapReduce [J]. Application Research of Computers, 2021, 38 (3): 689-695. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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