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Journal article
Positive and negative association rule mining in Hadoop’s MapReduce environment
Journal of big data, Vol.6(1), pp.1-16
08/22/2019
Web of Science ID: WOS:000599139200001
Abstract
In this paper, we present a Hadoop implementation of the Apriori algorithm. Using Hadoop’s distributed and parallel MapReduce environment, we present an architecture to mine positive as well as negative association rules in big data using frequent itemset mining and the Apriori algorithm. We also analyze and present the results of a few optimization parameters in Hadoop’s MapReduce environment as it relates to this algorithm. The results are presented based on the number of rules generated as well as the run-time efficiency. We find that, a higher amount of parallelization, which means larger block sizes, will increase the run-time efficiency of the Hadoop implementation of the Apriori algorithm.
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- Title
- Positive and negative association rule mining in Hadoop’s MapReduce environment
- Publication Details
- Journal of big data, Vol.6(1), pp.1-16
- Resource Type
- Journal article
- Publisher
- Springer International Publishing
- Format
- link
- Copyright
- This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Identifiers
- WOS:000599139200001; 99380178994106600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Computer Science
- Language
- English