CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Implement Mapreduce Apriori Algorithm to Generate Frequent Itemsets

by Jyoti Yadav, Neha Sehta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 38
Year of Publication: 2018
Authors: Jyoti Yadav, Neha Sehta
10.5120/ijca2018916785

Jyoti Yadav, Neha Sehta . Implement Mapreduce Apriori Algorithm to Generate Frequent Itemsets. International Journal of Computer Applications. 179, 38 ( Apr 2018), 7-10. DOI=10.5120/ijca2018916785

@article{ 10.5120/ijca2018916785,
author = { Jyoti Yadav, Neha Sehta },
title = { Implement Mapreduce Apriori Algorithm to Generate Frequent Itemsets },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 179 },
number = { 38 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number38/29322-2018916785/ },
doi = { 10.5120/ijca2018916785 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:59:19.330116+05:30
%A Jyoti Yadav
%A Neha Sehta
%T Implement Mapreduce Apriori Algorithm to Generate Frequent Itemsets
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 38
%P 7-10
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For Mass data cloud computing is used as solution for storing and analyzing .Cloud is suitable for big data computation for processing large parallel data sets. Hadoop is used for distributed computing that would be required to enable big data. MapReduce is a component of Hadoop used for parallel processing. In this paper strategy of mining association rules is discussed with apriori algorithm. Modified MapReduce apriori algorithm with TopDown approach is implemented on datasets .The results show that strategy designed has higher efficiency and takes less time for execution for calculating frequent itemsets.

References
  1. Sudhakar Singh ,Review of Apriori Based Algorithms on Map Reduce Framework.ICC-2014,pp 593-604
  2. Sruthi M,Parallel Implementation of modified Apriori Algorithms on Multicore Systems,Proceeding of IMCIC-ICSIT 2016.
  3. Apache hadoop.http:/hadoop.apache.org.
  4. Shunli Ding,A comprehensive Evaluation System of Association Rules on Multi-Index,(978-1-4673-6593-2/15)2015 IEEE
  5. M.Afzali,Hadoop-MapReduce:A Platform for finding Large datasets,978-9-3805-4421-2/16/$31.00 c 2016 IEEE”.
  6. Anil R.Surve,Hadoop-HBase for Finding Association Rulesusing MapReduce Apriori Algorithm,2016 IEEE.
  7. S.Maheshwari,Novel Method of Apriori Algorithm using Top Down Approach,International Journal of Computer Applications(0975-8887)volume 77-N
  8. Othman Yaha,An Efficient Implementation of Apriori Algorithm based on Hadoop-MapReduce Model. International Journal of Reviews in Computing 31st December 2012. Vol. 12 © 2009 - 2012 IJRIC & LLS.
  9. Sadhana Shetty, Implementation of Modified APRIORI Algorithm Using HADOOP ,Journal of Data Mining and Knowledge Engineering Volume 1 Issue 2Page 1-11© MANTECH PUBLICATIONS
  10. Xueyan Lin.MR-Apriori:Association Rules Algorithm Based on MapReduce.978-1-4799-3279-5/14/$31.00 ©
Index Terms

Computer Science
Information Sciences

Keywords

Hadoop Map-Reduce Apriori algorithm Data mining Support Association rules Top Down Approach.

Powered by PhDFocusTM