Parallel Association Rules Pruning Algorithm on Hadoop MapReduce

Mohamed A. Alasow; Salahadin A. Mohammed; El Sayed M. El-Alfy

doi:10.1007/978-981-15-3852-0_8

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 proceeding › Chapter › peer-review

1 Scopus citations

Abstract

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 language	English
Title of host publication	Lecture Notes in Networks and Systems
Publisher	Springer
Pages	117-130
Number of pages	14
DOIs	https://doi.org/10.1007/978-981-15-3852-0_8
State	Published - 2020

Publication series

Name	Lecture Notes in Networks and Systems
Volume	125
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.

Keywords

Association rules
Clustering
Data mining
Hadoop MapReduce
Knowledge discovery
Pruning

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-15-3852-0_8

Cite this

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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. 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.",

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Parallel Association Rules Pruning Algorithm on Hadoop MapReduce

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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