Abstract: 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.