DocumentCode
3065769
Title
The Strategy of Mining Association Rule Based on Cloud Computing
Author
Li, Lingjuan ; Zhang, Min
Author_Institution
Coll. of Comput., Nanjing Univ. of Posts & Telecommun., Nanjing, China
fYear
2011
fDate
29-31 July 2011
Firstpage
475
Lastpage
478
Abstract
Cloud computing provides cheap and efficient solutions of storing and analyzing mass data. It is very important to research the data mining strategy based on cloud computing from the theoretical view and practical view. In this paper, the strategy of mining association rules in cloud computing environment is focused on. Firstly, cloud computing, Hadoop, MapReduce programming model, Apriori algorithm and parallel association rule mining algorithm are introduced. Then, a parallel association rule mining strategy adapting to the cloud computing environment is designed. It includes data set division method, data set allocation method, improved Apriori algorithm, and the implementation procedure of the improved Apriori algorithm on MapReduce. Finally, the Hadoop platform is built and the experiment for testing performance of the strategy as well as the improved algorithm has been done. The results show that the strategy designed in this paper can archive higher efficiency when doing frequent item set mining in cloud computing environment.
Keywords
cloud computing; data analysis; data mining; parallel algorithms; Hadoop; MapReduce programming model; cloud computing; data analysis; data mining strategy; data set allocation method; data set division method; data storage; improved Apriori algorithm; parallel association rule mining algorithm; Algorithm design and analysis; Association rules; Cloud computing; Itemsets; Partitioning algorithms; Random access memory; Apriori; Association Rule Mining; Cloud Computing; MapReduce; Parallel;
fLanguage
English
Publisher
ieee
Conference_Titel
Business Computing and Global Informatization (BCGIN), 2011 International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4577-0788-9
Electronic_ISBN
978-0-7695-4464-9
Type
conf
DOI
10.1109/BCGIn.2011.125
Filename
6003927
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