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Mathematical Problems in Engineering
Volume 2013, Article ID 323750, 8 pages
Research Article

A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management

1School of Management, Shandong University, Jinan, Shandong 250100, China
2Department of Mathematics, West Virginia University, Morgantown, WV 26506, USA
3Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada V8W 2Y2
4Foundation Department, Shandong College of Electronic Technology, Jinan, Shandong 250200, China

Received 15 July 2013; Accepted 7 August 2013

Academic Editor: Yoshinori Hayafuji

Copyright © 2013 Peixin Zhao 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.


Community detection in social networks plays an important role in cluster analysis. Many traditional techniques for one-dimensional problems have been proven inadequate for high-dimensional or mixed type datasets due to the data sparseness and attribute redundancy. In this paper we propose a graph-based clustering method for multidimensional datasets. This novel method has two distinguished features: nonbinary hierarchical tree and the multi-membership clusters. The nonbinary hierarchical tree clearly highlights meaningful clusters, while the multimembership feature may provide more useful service strategies. Experimental results on the customer relationship management confirm the effectiveness of the new method.