An adaptive clustering algorithm based on data field in complex networks

Clustering analysis is a hot research in the field of complex network, in order to overcome high time complexity, difficulty for the user to select initial conditions and other defects of the existing clustering algorithms, this paper analyses the above problems and proposes an adaptive clustering algorithm based on data field in complex networks. First, the importance factor is proposed to dig out the important vertices in networks as the center of the cluster which is based on the defects and merits of evaluation indexes of the vertex´s degree, mutual information and closeness respectively. Due to the vertices in networks connected and react upon one another, the theory of data field in physics was introduced into complex networks, by calculating field-strength and potential function of vertices to realize clustering of vertices—cluster topology structure division. Simulation experiments show that the adaptive algorithm can get approximate optical cluster topology structures with a low time complexity, and has a higher accuracy and validity compared to other algorithms.