An Efficient Algorithm Based on MapReduce For Computing Frequent Item sets

DOI

10.2991/iccsee.2013.426How to use a DOI?

Keywords

association rules, frequent item sets, mapreduce, partition algorithm

Abstract

As 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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Da-wei JIN
AU  - Jie SONG
AU  - Bin LIU
AU  - Cheng ZHAO
PY  - 2013/03
DA  - 2013/03
TI  - An Efficient Algorithm Based on MapReduce For Computing Frequent Item sets
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1703
EP  - 1706
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.426
DO  - 10.2991/iccsee.2013.426
ID  - JIN2013/03
ER  -