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Eberly College of Arts and Sciences




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.

Source Citation

Zhao, P., Zhang, C.-Q., Wan, D., & Zhang, X. (2013). A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management. Mathematical Problems in Engineering, 2013, 1–8.


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.



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