DocumentCode :
3664069
Title :
An efficient Frequent Patterns Mining Algorithm based on MapReduce Framework
Author :
Run-Ming Yu; Ming-Gong Lee; Yuan-Shao Huang; Shi-Xuan Chen
Author_Institution :
Dept. of Comput. Sci. &
fYear :
2014
Firstpage :
1
Lastpage :
5
Abstract :
Recently, data collected from business have continuously growing in every enterprise. The Big Data, Cloud Computing, Data Mining has become hot topics at the present day. How to acquire important information quickly from these data is a critical issue. In this paper, we modified the traditional Apriori algorithm by improving the execution efficiency, since Aprori algorithm has confronted with a drawback that the computation time increases dramatically when data size increases. Since the one-phase algorithm only used one MapReduce operation, it will generate excessive candidates and result in insufficient memory. We design and implement an efficient algorithm: Frequent Patterns Mining Algorithm Based on MapReduce Framework (FAMR). We adopt Hadoop MapReduce as the experiment platform. The experiment results have shown that FAMR has 16.2 speedup at last in the running time compared with one-phase algorithm.
Publisher :
iet
Conference_Titel :
Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things 2014, International Conference on
Print_ISBN :
978-1-84919-970-4
Type :
conf
DOI :
10.1049/cp.2014.1525
Filename :
7284209
Link To Document :
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