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

Zhe-jun KUANG; Hang ZHOU; Dong-dai ZHOU; Jin-peng ZHOU; Kun YANG

doi:10.1631/FITEE.1800467

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

Regular Papers | Updated：2022-05-19

- A non-group parallel frequent pattern mining algorithm based on conditional patterns
- 基于条件模式的一种无分组并行频繁模式挖掘算法
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 9, Pages: 1234-1245(2019)
- Affiliations：
  
  College of Computer Science and Technology, Changchun University, Changchun 130022, China
  School of Economics, Changchun University, Changchun 130022, China
  School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
  Division of Engineering Science, University of Toronto, Ontario M5S2E8, Canada
  School of Computer Science and Electronic Engineering, University of Essex, Colchester CO43SQ, UK
- Author bio：
  
  [ "Zhe-jun KUANG, E-mail: kuangzhejun@ccu.edu.cn" ]
  [ "Hang ZHOU, E-mail:zhouhang0311@163.com" ]
  Dong-dai ZHOU, E-mail:ddzhou@nenu.edu.cn
- Funds：
- DOI：10.1631/FITEE.1800467
  CLC： TP301
- Received：05 August 2018，
  
  Revised：2019-;8-23，
  
  Published：2019-09
- Accepted：
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Zhe-jun KUANG, Hang ZHOU, Dong-dai ZHOU, et al. A non-group parallel frequent pattern mining algorithm based on conditional patterns[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1234-1245.
DOI：

Zhe-jun KUANG, Hang ZHOU, Dong-dai ZHOU, et al. A non-group parallel frequent pattern mining algorithm based on conditional patterns[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1234-1245. DOI： 10.1631/FITEE.1800467.

摘要

频繁项集挖掘是关联规则挖掘的主要方法。由于计算空间和性能限制，特别是当数据集剧增时，挖掘频繁项的关联需要大量时间和资源。在大数据环境下的关联数据挖掘过程中，通常采用MapReduce模型进行任务划分及并行处理，从而提高算法执行效率。为确保关联规则在任务划分和并行处理期间不被破坏，需要将内部关系数据存储在计算机空间中。与原始数据集相比，存储冗余的内部关系数据将显著增加空间的使用。研究发现，频繁模式挖掘算法的形成主要依赖于条件模式基。基于并行频繁模式（PFP）算法理论，本文提出一种无分组的PFP（NG-PFP）挖掘算法。该算法取消了分组模式，减少了子任务之间的数据冗余。实验结果表明，无分组模型在计算效率和空间利用率方面都有显著提高。

Abstract

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.

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