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Vol. 172, Issue 6, June 2014, pp. 295-300

 

Bullet

 

Detecting Community Structure in Complex Networks Using Bacterial Chemotaxis
with Fuzzy C-means Clustering
 

Yanling Li, Lei You, Gang Li

College of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Tel.: 15837601416, fax: (0376) 6390706

E-mail: lyl75@163.com

 

Received: 30 March 2014 /Accepted: 30 May 2014 /Published: 30 June 2014

Digital Sensors and Sensor Sysstems

 

Abstract: Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, the bacterial chemotaxis (BC) strategy is used to maximize the modularity of a network, associating with a dissimilarity-index-based and with a diffusion-distance-based fuzzy c-means clustering iterative procedure. The proposed algorithm outperforms most existing methods in the literature as regards the optimal modularity found. Experimental results indicate that the new algorithm is efficient at detecting both good clusterings and the appropriate number of clusters.

 

Keywords: Complex networks, Community structure, Modularity, Bacterial Chemotaxis, Fuzzy c-means clustering.

 

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