A clustering algorithm based on minimum spanning tree with e-learning applications

Siyang WANG, Zeping TANG, Yanghui RAO, Haoran XIE, Fu Lee WANG

Research output: Contribution to conferencePaper


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.
Original languageEnglish
Publication statusPublished - Nov 2015



Wang, S., Tang, Z., Rao, Y., Xie, H., & Wang, F. L. (2015, November). A clustering algorithm based on minimum spanning tree with e-learning applications. Paper presented at The 14th International Conference on Web-based Learning (ICWL 2015), South China University of Technology, Guangzhou, China.


  • Clustering
  • Density
  • Minimum spanning tree
  • E-learning