Algorithm Research & Explore
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1059-1063,1067

Novel distributed itemset mining algorithm based on item encoding

Zheng Jingyi
Deng Xiaoheng
College of Software, Central South University, Changsha 410075, China

Abstract

Apriori is one of the most widely used algorithm to discover frequent patterns. However, scanning the entire dataset in each iteration makes this algorithm inefficient and hard to be in parallel. With the size of datasets gets larger continuously, this problem is becoming more and more serious. Therefore, this paper proposed a novel algorithm called IEBDA. This algorithm was a kind of parallelization of Apriori based on item encoding and Spark framework. Saving information of each itemset by item encoding so that it could finish frequent itemset mining without scanning the whole dataset repeatedly. The broadcast variables of Spark enabled this algorithm to be in parallel. Compared with other distributed Apriori algorithms on datasets with different sizes, the acceleration of mining after the first iteration was obvious. The results show that this algorithm efficiently improves the multi-iteratively frequent itemset mining in big data environment.

Foundation Support

中南大学研究生科研创新项目(2017zzts612)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.11.0791
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 4
Section: Algorithm Research & Explore
Pages: 1059-1063,1067
Serial Number: 1001-3695(2019)04-022-1059-05

Publish History

[2019-04-05] Printed Article

Cite This Article

郑静益, 邓晓衡. 基于项编码的分布式频繁项集挖掘算法 [J]. 计算机应用研究, 2019, 36 (4): 1059-1063,1067. (Zheng Jingyi, Deng Xiaoheng. Novel distributed itemset mining algorithm based on item encoding [J]. Application Research of Computers, 2019, 36 (4): 1059-1063,1067. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

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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