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Association Rule Mining of Classified and Clustered Data of e-Learning System

Author(s): Sunita B Aher | LOBO L.M.R.J.

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: icwet;
Issue: 10;
Date: 2012;
Original page

Keywords: Weka | ADTree Classification Algorithm | Simple K-means Algorithm | Apriori Association Rule Algorithm | Learning management System

Classification is the supervised learning algorithm that maps the data into predefined groups & classes. Clustering is unsupervised algorithm which finds groups of objects such that the objects in one group will be similar to one another and different from the objects in another group while Association Rule algorithms are used to show the relationship between the data items. In this paper we propose the combination of three data mining algorithms: ADTree classification algorithm, Simple K-means Clustering Algorithm & Apriori Association Rule algorithm to recommend the course selected by the student. Here we consider the real sample data of Moodle courses of our college & check the result using the open source data mining tool Weka. First we classify the data using ADTree classification algorithm & then we apply the Simple k-means algorithm to the resultant data to obtain clusters. We apply the Apriori Association Rule algorithm on clusters obtained to find the best combination of courses which gives the better result as compare to result we obtained using only the Apriori Association Rule.

Tango Jona
Tangokurs Rapperswil-Jona

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