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

Modelling Academic Resources: An Apriori Approach

by Zunera Farooq, Vinod Sharma, Muheet Ahmed Butt
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 144 - Number 9
Year of Publication: 2016
Authors: Zunera Farooq, Vinod Sharma, Muheet Ahmed Butt
10.5120/ijca2016910426

Zunera Farooq, Vinod Sharma, Muheet Ahmed Butt . Modelling Academic Resources: An Apriori Approach. International Journal of Computer Applications. 144, 9 ( Jun 2016), 12-17. DOI=10.5120/ijca2016910426

@article{ 10.5120/ijca2016910426,
author = { Zunera Farooq, Vinod Sharma, Muheet Ahmed Butt },
title = { Modelling Academic Resources: An Apriori Approach },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 144 },
number = { 9 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 12-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume144/number9/25206-2016910426/ },
doi = { 10.5120/ijca2016910426 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:47:10.766253+05:30
%A Zunera Farooq
%A Vinod Sharma
%A Muheet Ahmed Butt
%T Modelling Academic Resources: An Apriori Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 144
%N 9
%P 12-17
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

“Data Mining or Knowledge Discovery is the process of discovering patterns in large data sets” [1] in the form databases and data warehouses in structured or unstructured manner. Association Rule Mining (ARM) is primarily focused on analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most meaningful relationships. In the area of academic data mining, it concerns with developing methods for discovering knowledge from data that come from Academic Enterprise Domain. There are several data mining algorithms pertaining to association rules used both offline and online platforms. One of the most popular and classical is Apriori algorithm that is used to extract frequent itemsets from large database and generating the association rule for discovering the knowledge. In the proposed research we have implemented an Apriori Algorithm implementation using Matlab and Dot Net Technologies using an academic examination registration dataset. The various Association Rules have been used to mine valuable knowledge regarding present, past and future course selection trends on subjects selected by the students at undergraduate level. The results will provide an insight in making future decisions regarding proposing academic infrastructure pertaining to human resource development/management, building of new departments/ centers, enhancing/ reducing intake capacity for a course/subject etc in an optimized manner.

References
  1. Bhargava, M., & Selwal, A. (2013). Association Rule mining using Apriori Algorithm: A review. International Journal of Advanced Research in Computer Science, 4(2).
  2. Jianking, N. (2013). Application of apriori algorithm to customer analysis. Information Technology Journal, 12(6), 55-59.
  3. Oguz, D., Yildiz, B., & Ergenc, B. (2013). DMA: Matrix Based Dynamic Itemset Mining Algorithm. International Journal of Data Warehousing and Mining (IJDWM), 9(4), 62-75.
  4. Wang, P. J., Shi, L., Bai, J. N., & Zhao, Y. L. (2009, December). Mining association rules based on apriori algorithm and application. In Computer Science-Technology and Applications, 2009. IFCSTA'09. International Forum on (Vol. 1, pp. 141-143). IEEE.
  5. R. Agrawal, “Mining Association Rules between Sets of Items in Large Databases,” ACM SIGMOD Int. Conf. Manag. Data, vol. 22, no. 02, pp. 207–216, 1993
  6. B. K. Baradwaj, “Mining Educational Data to Analyze Students ‟ Performance,” Int. J. Adv. Comput. Sci. Appl., vol. 2, no. 6, pp. 63–69, 2011.
  7. Galit.et.al, “Examining online learning processes based on log files analysis: a case study”. Research, Reflection and Innovations in Integrating ICT in Education 2007.
  8. U. K. Pandey, and S. Pal, “A Data mining view on classroom teaching language”, (IJCSI) International Journal of Computer Science Issue, Vol. 8, Issue 2, pp. 277-282, ISSN:1694-0814, 2011.
  9. M. M. A. Tair and A. M. El-halees, “Mining Educational Data to Improve Students ’ Performance : A Case Study,” vol. 2, no. 2, pp. 140–146, 2012.
  10. Chandra, E. and Nandhini, K. (2010) ‘Knowledge Mining from Student Data’, European Journal of Scientific Research, vol. 47, no. 1, pp. 156-163.
  11. S. V. Cristóbal Romero, “Educational Data Mining: A Review of the State of the Art,” IEEE Trans., vol. 20, no. 10, pp. 1–19, 2010.
  12. S. Khadijah and Z. Tasir, “Educational data mining : A review,” Procedia - Soc. Behav. Sci., vol. 97, pp. 320–324, 2013.
  13. Parack, S., Zahid, Z., & Merchant, F. (2012, January). Application of data mining in educational databases for predicting academic trends and patterns. In Technology Enhanced Education (ICTEE), 2012 IEEE International Conference on (pp. 1-4). IEEE.
  14. Wang, H., Liu, P., & Li, H. (2014, June). Application of improved association rule algorithm in the courses management. In Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on (pp. 804-807). IEEE.
  15. Bhandari, P., Withana, C., Alsadoon, A., & Elchouemi, A. (2015, October). Enhanced Apriori Algorithm model in course suggestion system. InComputing and Communication (IEMCON), 2015 International Conference and Workshop on (pp. 1-5). IEEE.
  16. Mirajkar, A. M., Sankpal, A. P., Koli, P. S., Patil, R. A., & Pradnyavant, A. R. (2016). Data Mining Based Store Layout Architecture for Supermarket.
Index Terms

Computer Science
Information Sciences

Keywords

Association rule mining Support Confidence Lift Apriori Algorithm.

Powered by PhDFocusTM