Apriori Algorithm Optimization Study Based on MapReduce

DOI

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chunqing Li
PY  - 2015/04
DA  - 2015/04
TI  - Apriori Algorithm Optimization Study Based on MapReduce
BT  - Proceedings of the 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  - 10.2991/amcce-15.2015.261
ID  - Li2015/04
ER  -