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DOI: 10.14569/IJACSA.2021.0120364
PDF

Clustering of Association Rules for Big Datasets using Hadoop MapReduce

Author 1: Salahadin A. Moahmmed
Author 2: Mohamed A. Alasow
Author 3: El-Sayed M. El-Alfy

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 3, 2021.

  • Abstract and Keywords
  • How to Cite this Article
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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%.

Keywords: Internet of Things; big data mining; clustering; association rules; Hadoop

Salahadin A. Moahmmed, Mohamed A. Alasow and El-Sayed M. El-Alfy, “Clustering of Association Rules for Big Datasets using Hadoop MapReduce” International Journal of Advanced Computer Science and Applications(IJACSA), 12(3), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120364

@article{Moahmmed2021,
title = {Clustering of Association Rules for Big Datasets using Hadoop MapReduce},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120364},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120364},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {3},
author = {Salahadin A. Moahmmed and Mohamed A. Alasow and El-Sayed M. El-Alfy}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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