Minimum Monotonous Constraint Closure Hadoop Parallel Association Rules Under Big Data Environment

DOI

10.2991/iccmcee-15.2015.144How to use a DOI?

Keywords

Big Data; Closure Operator; Minimum Monotonous Constraint; Hadoop Framework

Abstract

Aiming at the lager rule redundancy problems in traditional association rules, this article proposes minimum monotonous constraint closure Hadoop parallel association rules. First, basing on closure operator constraint rule equivalence relation set, this article gives satisfying minimum monotonous constraint rule set which can effectively divide the constraint rule set into disjoint equivalence rule class to reduce the rate of redundancy rule. Second, aiming at the big data problems, this article adopts Mapreduce parallel computation model under Hadoop framework to realize the parallelization computation of minimum monotonous constraint association rules which effectively promote the expansibility of algorithm to big data treatment. At last, through experimental comparison on standard test set, this article shows the effectiveness of the proposed algorithm.

Copyright

© 2015, 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  - Ou Jin
AU  - YiQiao Jin
AU  - Jianbiao He
AU  - Xi Li
PY  - 2015/11
DA  - 2015/11
TI  - Minimum Monotonous Constraint Closure Hadoop Parallel Association Rules Under Big Data Environment
BT  - Proceedings of the 2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 792
EP  - 796
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccmcee-15.2015.144
DO  - 10.2991/iccmcee-15.2015.144
ID  - Jin2015/11
ER  -