The week's pick
Reseach Article
A Comparative Study of Association Rule Algorithms for Course Recommender System in E-learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 39 - Number 1 |
| Year of Publication: 2012 |
| Authors: Sunita B Aher, Lobo L.M.R.J |
10.5120/4788-7021
|
Sunita B Aher, Lobo L.M.R.J . A Comparative Study of Association Rule Algorithms for Course Recommender System in E-learning. International Journal of Computer Applications. 39, 1 ( February 2012), 48-52. DOI=10.5120/4788-7021
Abstract
A course Recommender System plays an important role in predicting the course selection by student. Here we consider the real data from Moodle course of our college & we try to obtain the result using Weka. Association rule algorithms are used to find out the best combination of courses in E-Learning. Here in this paper we consider four association rule algorithms: Apriori Association Rule, PredictiveApriori Association Rule, Tertius Association Rule & Filtered Associator. We compare the result of these four algorithms & present the result. According to our simulation result, we find that Apriori association algorithms perform better than the Predictive Apriori Association Rule, Tertius Association Rule, & Filtered Associator in predicting the course selection based on student choice.
References
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- Gurpreet Kaur1, Naveen Aggarwal2:” Association Rule Mining in XML databases: Performance Evaluation and Analysis” IJCST Vol. 1, Issue 2, December 2010 ISSN : 2 229-4333 ( Print ) | ISSN : 0976 - 8491 (Online )
- Aman Kumar Sharma, Suruchi Sahni:” A Comparative Study of Classification Algorithms for Spam Email Data Analysis” in International Journal of computer science & Engineering ISSN: 0975-3397 Vol. 3 No. 5 May 2011.
- “Data Mining Introductory and Advanced Topics” by Margaret H. Dunham
- Weka (2007). http://www.cs.waikato.ac.nz/ml/weka/.
- Sunita B Aher and Lobo L.M.R.J.. Data Mining in Educational System using WEKA. IJCA Proceedings on International Conference on Emerging Technology Trends (ICETT) (3):20-25, 2011. Published by Foundation of Computer Science, New York, USA (ISBN: 978-93-80864-71-13)
- Sunita B Aher and Lobo L.M.R.J. Article: A Framework for Recommendation of courses in E-learning System. Selected in International Journal of Computer Applications Published by Foundation of Computer Science, New York, USA ISSN 0975 – 8887
- http://www.cs.sunysb.edu/~cse634/lecture_notes/07apriori.pdf accessed on date 02-02-2012
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