Hadoop based mining of distributed association rules from big data

2017 ◽  
Author(s):  
Marwa Bouraoui ◽  
Ines Bouzouita ◽  
Amel Grissa Touzi
Keyword(s):  
Big Data ◽  
Association Rules ◽  
Distributed Association

MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems

2018 ◽  
Vol 153 ◽  
pp. 176-192 ◽  
Author(s):  
D. Martín ◽  
M. Martínez-Ballesteros ◽  
D. García-Gil ◽  
J. Alcalá-Fdez ◽  
F. Herrera ◽  
...  
Keyword(s):  
Big Data ◽  
Association Rules ◽  
Mapreduce Framework ◽  
Quantitative Association Rules

Research of Association Rules Algorithm Based on Matrix under Cloud Computing

2014 ◽  
Vol 568-570 ◽  
pp. 798-801
Author(s):  
Ye Qing Xiong ◽  
Shu Dong Zhang
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Computing Technology ◽  
Binary Matrix ◽  
Transaction Data ◽  
Big Data Mining ◽  
Parallel Mining

It occurs time and space performance bottlenecks when traditional association rules algorithms are used to big data mining. This paper proposes a parallel algorithm based on matrix under cloud computing to improve Apriori algorithm. The algorithm uses binary matrix to store transaction data, uses matrix "and" operation to replace the connection between itemsets and combines cloud computing technology to implement the parallel mining for frequent itemsets. Under different conditions, the simulation shows it improves the efficiency, solves the performance bottleneck problem and can be widely used in big data mining with strong scalability and stability.

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