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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.

References
  1. Brijesh Kumar Baradwaj, Sourabh Pal, "Mining Educational Data to Analyze Student's Performance", IJACSA, Volume 2, Nov - 2011.
  2. Han, J. and Kamber, "Data Mining: Concepts and Techniques", 2nd edition. The Morgan Kaufmann Series in Data Management Systems", Jim Gray, Series Editor, 2006.
  3. Agrawal, Rakesh, Imielinski, Tomasz, Swami, Arun, "Mining Association Rules between Sets of Items in Large Databases", Proceedings of the ACM SIGMOD International Conference on Management of Data, 1993.
  4. J. Han and M. Kamber, " Data Mining: Concepts and Techniques", Morgan Kaufmann, 2000.
  5. Shyamala K. and Rajagopalan S. P. , "Data Mining Model for a better Higher Educational System", Information Technology Journal, Vol. 5, No. 3, pp. 560-564, 2006.
  6. Ranjan J. and Malik K. , "Effective Educational Process: A Data Mining Approach", Vol. 37, Issue 4, pp 502-515, 2007.
  7. M. Ramaawami and R. Bhaskaran, "A CHAID based performance prediction model in educational data mining", 2010.
  8. J. Mamcenko, Jelena, Irna Sileikiene, Jurgita Lieponiene and Regina Kulvietiene, "Analysis of E-Exam data using data mining techniques", proceeding of 17th international conference on information and software technologies, pp. 215-219, 2011.
  9. T. Hadzilacos, D. Kalles, C. Pierrakeas and M. Xenos, "On small data sets revealing big differences", Advance in artificial intelligence, pp. 512-515, 2006.
  10. Azhar Rauf, Sheeba, "Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity", Middle-East Journal of Scientific Research, Vol. 12, Pp. 959-963, 2012.
  11. Jaideep Vaidya, "Privacy Preserving K-Means Clustering over Vertically Partitioned Data", proceeding of SIGKDD, Washington, DC, USA, August 24-27, 2003.
  12. S. A. Kumar and M. N. Vijayalakshmi, "Efficency of decision tree in predicting student's academic performance", first international conference on computer science, Engineering and applications, Vol. 2, pp. 335-343, 2011.
  13. Ceglar, Aaron, Roddick, John F. , "Association mining: ACM Computing Surveys ", Volume 38 , Issue 2, 2006.
  14. Liu, Bing, "Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data", Springer, 2007.
  15. Agrawal, Rakesh, Srikant, Ramakrishnan, "Fast Algorithms for Mining Association Rules", Proceedings 20th Int. Conf. on Very Large Data Bases, VLDB, 1994.
  16. Borgelt, Christian, "An Implementation of the FP-growth Algorithm", ACM Press, New York, NY, USA, 2005.
  17. Ceglar, Aaron, Roddick, John F. , "Association mining: ACM Computing Surveys ", Volume 38 , Issue 2, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Educational data mining Association rule mining Data Transform.

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