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Generating Optimized Association Rule for Big Data Using GA and MLMS

Arsha Sultana1 , S. Madhavi2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-9 , Page no. 144-148, Sep-2015

Online published on Oct 01, 2015

Copyright © Arsha Sultana , S. Madhavi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Arsha Sultana , S. Madhavi , “Generating Optimized Association Rule for Big Data Using GA and MLMS,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.144-148, 2015.

MLA Style Citation: Arsha Sultana , S. Madhavi "Generating Optimized Association Rule for Big Data Using GA and MLMS." International Journal of Computer Sciences and Engineering 3.9 (2015): 144-148.

APA Style Citation: Arsha Sultana , S. Madhavi , (2015). Generating Optimized Association Rule for Big Data Using GA and MLMS. International Journal of Computer Sciences and Engineering, 3(9), 144-148.

BibTex Style Citation:
@article{Sultana_2015,
author = {Arsha Sultana , S. Madhavi },
title = {Generating Optimized Association Rule for Big Data Using GA and MLMS},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2015},
volume = {3},
Issue = {9},
month = {9},
year = {2015},
issn = {2347-2693},
pages = {144-148},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=657},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=657
TI - Generating Optimized Association Rule for Big Data Using GA and MLMS
T2 - International Journal of Computer Sciences and Engineering
AU - Arsha Sultana , S. Madhavi
PY - 2015
DA - 2015/10/01
PB - IJCSE, Indore, INDIA
SP - 144-148
IS - 9
VL - 3
SN - 2347-2693
ER -

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Abstract

For mining association rule different algorithms are used such as Apriori, tree based algorithm which take too much computerized time to accomplish all the frequent items. These obstacles are eliminated by using GA and MLMS and also improving the performance. In this method used a multi level minimum support of data table as 0 and 1. Genetic algorithm is indiscriminate search algorithm model based on natural selection, works in an iteration manner and is very adequate in large amount of data. Genetic algorithm is implemented in Hadoop to reduce computation cost. Hadoop supports for manipulating large data and operate them in parallel manner for better performance. The optimal frequent items are access that satisfies fitness, support and confidence.

Key-Words / Index Term

Association Rule, Apriori algorithm, Genetic algorithm, Hadoop ,MapReduce

References

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