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Mathematical Problems in Engineering
Volume 2014, Article ID 676427, 9 pages
Research Article

Cluster Analysis Based on Bipartite Network

1School of Computer Science and Technology, Liaoning Normal University, Dalian Liaoning 116081, China
2School of Urban and Environmental Science, Liaoning Normal University, Dalian Liaoning 116029, China
3School of Psychology, Liaoning Normal University, Dalian Liaoning 116029, China

Received 11 November 2013; Accepted 27 December 2013; Published 10 February 2014

Academic Editor: Fuzhong Nian

Copyright © 2014 Dawei Zhang 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.


Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzy c-means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster number in advance. The method works by converting the fuzzy cluster result into a weighted bipartite network and then the optimal cluster number can be detected by the improved bipartite modularity. The experimental results on artificial and real data sets show the validity of the proposed method.