Clustering of Association Rules for Big Datasets using Hadoop MapReduce

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Mining association rules is essential in the discovery of knowledge hidden in datasets. There are many efficient association rule mining algorithms. However, they may suffer from generating large number of rules when applied to big datasets. Large number of rules makes knowledge discovery a daunting task because too many rules are difficult to understand, interpret or visualize. To reduce the number of discovered rules, researchers proposed approaches, such as rules pruning, summarizing, or clustering. For the flourishing field of big data and Internet-of-Things (IoT), more effective solutions are crucial to cope with the rapid evolution of data. In this paper, we are proposing a novel parallel association rule clustering approach which is based on Hadoop MapReduce. We ran many experiments to study the performance of the proposed approach, and promising results have been demonstrated, e.g. the lowest scaleup was 77%.

Original languageEnglish
Pages (from-to)536-545
Number of pages10
JournalInternational Journal of Advanced Computer Science and Applications
Volume12
Issue number3
DOIs
StatePublished - 2021

Bibliographical note

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

Publisher Copyright:
© 2021. All Rights Reserved.

Keywords

  • Hadoop
  • Internet of Things
  • association rules
  • big data mining
  • clustering

ASJC Scopus subject areas

  • General Computer Science

Fingerprint

Dive into the research topics of 'Clustering of Association Rules for Big Datasets using Hadoop MapReduce'. Together they form a unique fingerprint.

Cite this