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A Comparative Study for Selecting the Best Unsupervised Learning Algorithm in E-Learning System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 41 - Number 3 |
| Year of Publication: 2012 |
| Authors: Sunita B Aher, Lobo L.m.r.j |
10.5120/5523-7562
|
Sunita B Aher, Lobo L.m.r.j . A Comparative Study for Selecting the Best Unsupervised Learning Algorithm in E-Learning System. International Journal of Computer Applications. 41, 3 ( March 2012), 27-34. DOI=10.5120/5523-7562
Abstract
Clustering is one of the most important techniques of data mining. Clustering technique in data mining is an unsupervised machine learning algorithm that finds the groups of object such that objects in one group will be similar to one another and are dissimilar to the objects belonging to other clusters. Clustering is called unsupervised machine learning algorithm as groups are not predefined but defined by the data. So the most similar data are grouped into the clusters. In this paper, we compare five clustering algorithm namely Farthest first, MakeDensityBasedClusterer, Simple K-means, EM, Hierarchical clustering algorithm for recommending the course to the student based on student course selection & present the result. According to our simulation, we find that Simple K-means works better than other algorithms.
References
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