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Pattern Mining Approach to Categorization of Students' Performance using Apriori Algorithm

by Sushil Kumar Verma, R.s.thakur, Shailesh Jaloree
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 5
Year of Publication: 2015
Authors: Sushil Kumar Verma, R.s.thakur, Shailesh Jaloree
10.5120/21540-4550

Sushil Kumar Verma, R.s.thakur, Shailesh Jaloree . Pattern Mining Approach to Categorization of Students' Performance using Apriori Algorithm. International Journal of Computer Applications. 121, 5 ( July 2015), 36-39. DOI=10.5120/21540-4550

@article{ 10.5120/21540-4550,
author = { Sushil Kumar Verma, R.s.thakur, Shailesh Jaloree },
title = { Pattern Mining Approach to Categorization of Students' Performance using Apriori Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 5 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number5/21540-4550/ },
doi = { 10.5120/21540-4550 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:07:41.914717+05:30
%A Sushil Kumar Verma
%A R.s.thakur
%A Shailesh Jaloree
%T Pattern Mining Approach to Categorization of Students' Performance using Apriori Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 5
%P 36-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Researchers are used of data mining to extract hidden information from raw data. Now data mining can be used in any domain such as education. Data mining is used in education to achieve quality education and to categorize the students' performance through the analysis of educational data which reside or store in educational organization's database. In this paper, we categorize the performance of students based on their previous records such as 12th marks, graduation marks, previous semester marks (PSM) , previous academic records (PAR- average of 12th and graduation marks), mid sem marks (MSM), attendance (ATT) and end semester marks (ESM). Based on these attributes we determine the performance of students in end semester using apriori algorithm. With the help of categorization of performance, the main advantage is that classify of weak students, so that teacher give the particular interest on weak students and they could better perform in the next semester exam.

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Index Terms

Computer Science
Information Sciences

Keywords

Data mining Educational data mining Association rule mining Data Transform.

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