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Mining Association Rule in Classified Data for Course Recommender System in E-Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 39 - Number 7 |
| Year of Publication: 2012 |
| Authors: Sunita B. Aher, Lobo L.M.R.J |
10.5120/4829-7086
|
Sunita B. Aher, Lobo L.M.R.J . Mining Association Rule in Classified Data for Course Recommender System in E-Learning. International Journal of Computer Applications. 39, 7 ( February 2012), 1-7. DOI=10.5120/4829-7086
Abstract
The ADTree (Alternating Decision Tree) is a supervised classification technique that combines decision trees with the predictive accuracy into a set of classification rules & association rule algorithms are used to show the relationship between data items. Here in this paper we combine these two algorithms & apply it to sample data obtained from Moodle courses of our college for the Course Recommender System which predicts the course selected by the students. First we consider the result using only the association rule then we consider this combined approach. Here we present the advantage of applying the combined approach to Course Recommender System as compare to the result of applying only the association rule algorithm. We found that combined approach works better than only the association rule mining. This combined approach also increases the strength of the rules.
References
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- Alternating decision tree, available at: http://en.wikipedia.org/wiki/alternating_decision_tree Accessed on 13-02-2012
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