Full Text:   <424>

Summary:  <119>

CLC number: TP301

On-line Access: 2019-10-08

Received: 2018-08-05

Revision Accepted: 2018-12-18

Crosschecked: 2019-06-23

Cited: 0

Clicked: 811

Citations:  Bibtex RefMan EndNote GB/T7714


Zhe-jun Kuang


Dong-dai Zhou


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.9 P.1234-1245


A non-group parallel frequent pattern mining algorithm based on conditional patterns

Author(s):  Zhe-jun Kuang, Hang Zhou, Dong-dai Zhou, Jin-peng Zhou, Kun Yang

Affiliation(s):  College of Computer Science and Technology, Changchun University, Changchun 130022, China; more

Corresponding email(s):   kuangzhejun@ccu.edu.cn, zhouhang0311@163.com, kddzhou@nenu.edu.cn

Key Words:  Frequent pattern mining, Parallel algorithm, Conditional pattern bases, MapReduce, Big data

Zhe-jun Kuang, Hang Zhou, Dong-dai Zhou, Jin-peng Zhou, Kun Yang. A non-group parallel frequent pattern mining algorithm based on conditional patterns[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1234-1245.

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journal="Frontiers of Information Technology & Electronic Engineering",
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%A Dong-dai Zhou
%A Jin-peng Zhou
%A Kun Yang
%J Frontiers of Information Technology & Electronic Engineering
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800467

T1 - A non-group parallel frequent pattern mining algorithm based on conditional patterns
A1 - Zhe-jun Kuang
A1 - Hang Zhou
A1 - Dong-dai Zhou
A1 - Jin-peng Zhou
A1 - Kun Yang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 9
SP - 1234
EP - 1245
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Y1 - 2019
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1800467

Frequent itemset mining serves as the main method of association rule mining. With the limitations in computing space and performance, the association of frequent items in large data mining requires both extensive time and effort, particularly when the datasets become increasingly larger. In the process of associated data mining in a big data environment, the mapReduce programming model is typically used to perform task partitioning and parallel processing, which could improve the execution efficiency of the algorithm. However, to ensure that the associated rule is not destroyed during task partitioning and parallel processing, the inner-relationship data must be stored in the computer space. Because inner-relationship data are redundant, storage of these data will significantly increase the space usage in comparison with the original dataset. In this study, we find that the formation of the frequent pattern (FP) mining algorithm depends mainly on the conditional pattern bases. Based on the parallel frequent pattern (PFP) algorithm theory, the grouping model divides frequent items into several groups according to their frequencies. We propose a non-group PFP (NG-PFP) mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks. Moreover, we present the NG-PFP algorithm for task partition and parallel processing, and its performance in the Hadoop cluster environment is analyzed and discussed. Experimental results indicate that the non-group model shows obvious improvement in terms of computational efficiency and the space utilization rate.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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