ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model

Clustering is an important research field in data mining.Based on dynamical synchronization model,an efficient synchronization clustering algorithm ESYN is proposed.Firstly,based on local structure information of a non-vector network,a new concept vertex similarity is brought up to describe the link density between vertices.Secondly,the network is vectoried by OPTICS algorithm and turned into one-dimensional coordination sequence.Finally,global coupling analysis is applied to generalized Kuramoto synchronization model,synchronization radius is increased and the optimal clustering result is automatically selected.The experimental results on a large number of synthetic and real-world networks show that proposed algorithm achieves high accuracy.