IJERT-EMS
IJERT-EMS

Evaluation of Optimised Apriori Algorithm on HDFS using MapReduce in Hadoop Distributed Mode


Evaluation of Optimised Apriori Algorithm on HDFS using MapReduce in Hadoop Distributed Mode
Authors : Vvd Prasad Challuri, Blvv Kumar, K Purushotham Naidu, M Santosh
Publication Date: 04-07-2017

Authors

Author(s):  Vvd Prasad Challuri, Blvv Kumar, K Purushotham Naidu, M Santosh

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 6 - Issue. 06 , June - 2017

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV6IS060431

Abstract

With a revolutionary change in data analytics it requires techniques that can equally extend with the trending data processing methods. To keep in pace with this elated progress in information evaluation, calibration and storage patterns, development and implementation of large scale algorithms for data processing is gaining importance. In datamining, association rule mining and classification is a wellutilised methodology for identifying overwhelming relations from data in large scale analytics. Apriori algorithm is one such crucial algorithm to mine the frequent item sets which form the basis for finding association rules among the data. Analyzing frequent item sets is a crucial step to find rules and association between them. This stands as a primary foundation for crucial decision making. With the advent of Hadoop Map-Reduce, parallel processing and efficient memory utilisation has come into order. This paper aims to identify the potential of Apriori Algorithm which is implemented as one-phase and k-phase Apriori algorithms in MapReduce framework and further an Optimised Apriori Algorithms(OAA) has been implemented which has a full-fledged MapReduce benefits and it has been identified that Optimised Apriori Algorithm has yielded better efficiency and reduced time complexity.

Citations

Number of Citations for this article:  Data not Available

Keywords

Key Word(s):    

Downloads

Number of Downloads:     5
Similar-Paper

Call for Papers - May - 2017

        

 

                 Call for Thesis - 2017 

     Publish your Ph.D/Master's Thesis Online

              Publish Ph.D Master Thesis Online as Book