A Frequent Item Sets Computing Algorithm Based on MapReduce

Da Wei Jin; Jie Song; Bin Liu; Cheng Zhao

doi:10.4028/www.scientific.net/AMM.380-384.1701

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384A Frequent Item Sets Computing Algorithm Based on...

A Frequent Item Sets Computing Algorithm Based on MapReduce

Abstract:

Traditional method mining for association rules between items in large and grand data sets is inefficient. In this paper we present an efficient method called BPMRA which is based on mapreduce and partition. We have compared BPMRA algorithm based multi-node and partition based single node method and performed some experiments. It turns out that BPMRA possesses high parallelism good stability and scalability, especially suitable for mining for association rules in large and grand data sets.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

1701-1704

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.1701

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Online since:

August 2013

Authors:

Da Wei Jin, Jie Song, Bin Liu, Cheng Zhao

Keywords:

Frequent Item Set, MapReduce, Partition

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References

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