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Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop

by Than Htike Aung, Nang Saing Moon Kham, Soe Soe Mon
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 37
Year of Publication: 2020
Authors: Than Htike Aung, Nang Saing Moon Kham, Soe Soe Mon
10.5120/ijca2020920935

Than Htike Aung, Nang Saing Moon Kham, Soe Soe Mon . Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop. International Journal of Computer Applications. 175, 37 ( Dec 2020), 13-20. DOI=10.5120/ijca2020920935

@article{ 10.5120/ijca2020920935,
author = { Than Htike Aung, Nang Saing Moon Kham, Soe Soe Mon },
title = { Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2020 },
volume = { 175 },
number = { 37 },
month = { Dec },
year = { 2020 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number37/31691-2020920935/ },
doi = { 10.5120/ijca2020920935 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:40:30.758449+05:30
%A Than Htike Aung
%A Nang Saing Moon Kham
%A Soe Soe Mon
%T Improved Frequent Pattern Mining for Educational Data by using Mapreduce Approach in Hadoop
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 37
%P 13-20
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we describe the formatting guidelines for IJCA Journal Submission. In the education area of Myanmar, computers, mobile and internet have become important tools for high school students. To enable the quality and the flexibility of the education, verities of education programs and methods are greatly included but with different manners. In this paper, the field of large educational data and how big educational data can be analysis to provided quality improvement in education. For the frequent pattern mining and exploitation of educational data, proposed system present improved data mining techniques and popular applied hadoop mapreduce for large data manipulation such as parallel processing data analysis such as learning, academic and visual analytics, providing examples of how these techniques and methods could be used. The proposed system has been started pay attention to the teacher assessment application of data and data analytics to handle large data generated in the educational sector. These data stored in Hadoop file system, then discover frequent pattern by using mapreduce support apriori, eclat and prefix tree methods. These approached is effective and scalable for large data instead use of traditional standard data mining tools.

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Index Terms

Computer Science
Information Sciences

Keywords

Hadoop Mapreduce Eclat Apriori Prefix

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