Parallel Association Rules Pruning Algorithm on Hadoop MapReduce

Mohamed A. Alasow, Salahadin A. Mohammed, El Sayed M. El-Alfy*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations


Mining association rules is essential in the discovery of knowledge hidden in datasets. There are many efficient association rule mining algorithms. The problem is with the large number of rules they often discover. Large number of rules makes the discovery of knowledge very challenging because too many rules are difficult to understand, interpret, or visualize. To reduce the number of discovered rules, researchers proposed a number of solutions. However, these solutions are limited to the rules generated from traditional datasets and are incapable of handling rules generated from big datasets. To solve this problem, this paper proposes a Hadoop MapReduce-based parallel association rule pruning algorithm, named PPrune. Experimental results show that PPrune to be efficient and has good speedup, scaleup, and sizeup.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
Number of pages14
StatePublished - 2020

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Bibliographical note

Funding Information:
Acknowledgements The authors would like to thank King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, for the support during this work.

Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.


  • Association rules
  • Clustering
  • Data mining
  • Hadoop MapReduce
  • Knowledge discovery
  • Pruning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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