Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering

Apriori Algorithm Optimization Study Based on MapReduce

Authors
Chunqing Li
Corresponding Author
Chunqing Li
Available Online April 2015.
DOI
https://doi.org/10.2991/amcce-15.2015.261How to use a DOI?
Keywords
MapReduce;Apriori algorithm optimization; distributed; pruning
Abstract
To solve the deficiency of algorithm distributed association rules based on MapReduce, this paper introduces global pruning strategy to increase algorithm efficiency, adopts frequent matrix storage to reduce the consumption of internal storage, and puts forward MFMDAP of frequent matrix storage of MapReduce calculation model. Experiments show that the algorithm in the paper elevates the algorithm efficiency and saves the usage amount of internal storage, which is in favor of the calculation and storage of big granularity data. The effectiveness of algorithm has been approved in experiments.
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Proceedings
2015 International Conference on Automation, Mechanical Control and Computational Engineering
Part of series
Advances in Intelligent Systems Research
Publication Date
April 2015
ISBN
978-94-62520-64-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/amcce-15.2015.261How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Chunqing Li
PY  - 2015/04
DA  - 2015/04
TI  - Apriori Algorithm Optimization Study Based on MapReduce
BT  - 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.261
DO  - https://doi.org/10.2991/amcce-15.2015.261
ID  - Li2015/04
ER  -