Apriori Algorithm Optimization Study Based on MapReduce
- 10.2991/amcce-15.2015.261How to use a DOI?
- MapReduce;Apriori algorithm optimization; distributed; pruning
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.
- © 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/).
Cite this article
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 -