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Mathematical Problems in Engineering
Volume 2015, Article ID 747628, 14 pages
Research Article

A Coupled User Clustering Algorithm Based on Mixed Data for Web-Based Learning Systems

1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
2Digital Productivity, Commonwealth Scientific and Industrial Research Organisation, Sandy Bay, TAS 7005, Australia

Received 24 April 2015; Accepted 15 June 2015

Academic Editor: Francisco Alhama

Copyright © 2015 Ke Niu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In traditional Web-based learning systems, due to insufficient learning behaviors analysis and personalized study guides, a few user clustering algorithms are introduced. While analyzing the behaviors with these algorithms, researchers generally focus on continuous data but easily neglect discrete data, each of which is generated from online learning actions. Moreover, there are implicit coupled interactions among the data but are frequently ignored in the introduced algorithms. Therefore, a mass of significant information which can positively affect clustering accuracy is neglected. To solve the above issues, we proposed a coupled user clustering algorithm for Wed-based learning systems by taking into account both discrete and continuous data, as well as intracoupled and intercoupled interactions of the data. The experiment result in this paper demonstrates the outperformance of the proposed algorithm.