A parallel algorithm of association rules based on cloud computing

Author(s):  
Wang Yong ◽  
Zhang Zhe ◽  
Wang Fang
2014 ◽  
Vol 568-570 ◽  
pp. 798-801
Author(s):  
Ye Qing Xiong ◽  
Shu Dong Zhang

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.


This chapter aims at exploring the intersection of cloud computing with big data. The big data analysis, mining, and privacy concerns are discussed. First, this chapter deals with the software framework, MapReduce™ that is commonly used for performing Big Data Analysis in the clouds. In addition, some of the most used techniques for performing Big Data Mining are detailed. For instance, Clustering, Co-Clustering, and Association Rules are described in detail. In particular, the k-center problem is described while with reference to the association rules beyond the basic definitions, the Apriori Algorithm is outlined and illustrated by some numerical examples. These techniques are also described with reference to their versions based on MapReduce. Finally, the description of some real applications conclude the chapter.


Sign in / Sign up

Export Citation Format

Share Document