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: Chapter in Book/Report/Conference proceedingChapters

1 Citation (Scopus)

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. Copyright © 2016 Springer International Publishing Switzerland.
Original languageEnglish
Title of host publicationCurrent developments in web based learning: ICWL 2015 International Workshops, KMEL, IWUM, LA, Guangzhou, China, November 5-8, 2015, Revised Selected Papers
EditorsZhiguo GONG , Dickson K.W. CHIU , Di ZOU
Place of PublicationSwitzerland
PublisherSpringer International Publishing
Pages3-12
ISBN (Print)9783319328645, 9783319328652
DOIs
Publication statusPublished - 2016

Citation

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 Z. Gong, D. K. W. Chiu, & D. Zou (Eds.), Current developments in web based learning: ICWL 2015 International Workshops, KMEL, IWUM, LA, Guangzhou, China, November 5-8, 2015, Revised Selected Papers (pp. 3-12). Switzerland: Springer International Publishing.

Keywords

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

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