Abstract
The rapid development of web-based learning applications has generated large amounts of learning resources. Faced with this situation, clustering is valuable to group modeling and intelligent tutoring. In traditional clustering algorithms, the initial centroid of each cluster is often assigned randomly. Sometimes it is very difficult to get an effective clustering result. In this paper, we propose a new clustering algorithm based on a minimum spanning tree, which includes the elimination and construction processes. In the elimination phase, the Euclidean distance is used to measure the density. Objects with low densities are considered as noise and eliminated. In the construction phase, a minimum spanning tree is constructed to choose the initial centroid based on the degree of freedom. Extensive evaluations using datasets with different properties validate the effectiveness of the proposed clustering algorithm. Furthermore, we study how to employ the clustering algorithms in three different e-learning applications.
Y. Rao—The research work described in this article has been substantially supported by the Fundamental Research Funds for the Central Universities (Project Number: 46000-31610009).
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Acknowledgements
The research work described in this article has been supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14).
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Wang, S., Tang, Z., Rao, Y., Xie, H., Wang, F.L. (2016). A Clustering Algorithm Based on Minimum Spanning Tree with E-learning Applications. In: Gong, Z., Chiu, D., Zou, D. (eds) Current Developments in Web Based Learning. ICWL 2015. Lecture Notes in Computer Science(), vol 9584. Springer, Cham. https://doi.org/10.1007/978-3-319-32865-2_1
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DOI: https://doi.org/10.1007/978-3-319-32865-2_1
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